Audio and video overviews
Audio and video overviews used to turn source collections into podcasts, explainers, interactive learning media, and visual presentations.
32%
Best tweets about NotebookLM
50 Best Tweets About NotebookLM (2026)
Explore the best tweets about NotebookLM, including source-grounded research, audio overviews, study workflows, prompts, and practical use cases.
Specific NotebookLM workflows, features, research methods, source handling, and product updates supported by firsthand examples.
- Creators
- 41
- Updated
Original Xholic analysis
What 50 top NotebookLM posts reveal
The dataset is predominantly supportive (86% supportive stance; 88% positive sentiment) and focuses on source-grounded research, learning artifacts, and audio/video overviews. The most concrete feature-update evidence comes from direct announcement posts, while creator workflow stories offer useful prompt and source-handling patterns but should not be treated as independently verified performance results. Media posts have a higher median all-time score than text-only posts (41.39 vs. 24.45).
- Dominant tone
- Positive
- Median score
- 34.8
- Leading format
- Announcement
- Recent posts
- 30%
88% of posts
All-time engagement
30% of posts
Published in 90 days
Conversation map
The themes creators return to
Integrations and automation},{
NotebookLM integration with Gemini, Google Drive, Deep Research, Antigravity, Obsidian, APIs, and agentic research/build pipelines.
28%
Generated learning artifacts
Generated artifacts such as slides, infographics, mind maps, flashcards, quizzes, reports, tables, and visual explainers.
26%
Source-grounded research
Source-grounded research methods, including cross-document synthesis, comparisons, citations, timelines, and curated source collections.
26%
Advanced prompting and thinking
Prompt frameworks that move beyond summaries toward questioning, critique, gap analysis, mental models, and deeper reasoning.
20%
Learning and exam preparation
Study workflows that use uploaded lectures, readings, slides, and past materials to map concepts, diagnose gaps, generate quizzes, and prepare for exams.
14%
Professional domain workflows
Professional knowledge-base workflows for legal review, medical records, customer intelligence, agency calls, and domain-specific analysis.
14%
Book and long-form reading
Book and long-form reading workflows for extracting arguments, prioritizing chapters, challenging claims, and improving retention.
10%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 72
- Median reposts
- 10
- Median replies
- 7
- Median views
- 7.9K
Posts with media make up 76% of this collection. Their median all-time score is 41.4, compared with 24.4 for text-only posts.
Format mix
- Announcement 30% · score 81.5
- Tutorial 30% · score 21.9
- Case Study 28% · score 18.6
- Other 12% · score 195.2
Consensus and debate
Where creators agree, and where they do not
Shared view
Prompt-led study workflows emphasize connections and gaps
Several posts advocate prompts that go beyond summaries, including connecting concepts across uploaded materials, identifying knowledge gaps, and asking teaching- or critique-oriented questions. The detailed “context stacking” outcome is a reported student workflow, not independently verified evidence of study gains.
Shared view
Video-overview updates span cinematic and mobile use
A NotebookLM post announced Cinematic Video Overviews rolling out in English for Ultra users. Other cited posts say video-overview customization and mobile generation were added to the mobile app.
Shared view
Posts frame a source set as input to multiple artifacts
Posts describe turning source material into a range of outputs, including slides, infographics, mind maps, flashcards, quizzes, reports, audio, and video. Individual availability and capability claims should be checked in the product for a given plan and region.
Shared view
Curated source collections are a recurring research use case
Featured-notebook posts highlight curated collections of source material, including Google Research’s neuroscience notebook and a Royal Society collaboration on Benjamin Franklin. A separate post characterizes Gemini notebook answers as grounded in approved sources; that product claim is presented by the poster.
Open debate
Useful outputs still require review and revision
One student says classmates cite hallucination concerns when avoiding AI for study. Two users also describe correcting generated slides or revising prompts after an initial output, supporting a verification-and-iteration caution.
Open debate
Reported upcoming features remain provisional
Posts about Canvas, Connectors, and Personal Intelligence describe features as hidden, spotted in testing, or expected soon. They should not be read as confirmation of general availability.
What performs
Patterns behind standout posts
The five score outliers span workflows and product posts
The five deterministic score outliers cover a study-prompt narrative, Cinematic Video Overviews, a legal-review narrative, a Gemini–NotebookLM integration announcement, and a curated research notebook. The context-stacking study post is the highest-scoring outlier (6,266.94).
Announcements have the strongest format median
Announcements and tutorials each represent 30% of the dataset, while case studies represent 28%. Announcements have the higher median all-time score (81.487), versus 21.889 for tutorials and 18.59 for case studies.
Study-focused themes and media posts show stronger medians
Learning and exam preparation has the highest listed theme median all-time score (110.791), followed by generated learning artifacts (61.085) and advanced prompting and thinking (57.52). Posts with media also have a higher median score than text-only posts (41.39 vs. 24.45).
Statistical standouts
- View standout post 1 Score 6266.9 · 180.24× median
- View standout post 2 Score 3007.9 · 86.51× median
- View standout post 3 Score 1762.8 · 50.7× median
- View standout post 4 Score 771.0 · 22.18× median
- View standout post 5 Score 392.2 · 11.28× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Louis Gleeson
@aigleeson
2 posts
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2. Jainam Parmar
@aiwithjainam
2 posts
-
3. Amit | Frogomo | AI 🐸
@frog_omo
2 posts
-
4. Gemini Notebook
@Gemini_Notebook
2 posts
-
5. Glenn Gabe
@glenngabe
2 posts
-
6. Emily
@IamEmily2050
2 posts
Ihtesham Ali uses narrative workflow case studies
Ihtesham Ali’s two cited posts present narrative study and legal-review scenarios. Their large reported workflow improvements and performance outcomes are anecdotes within the posts, rather than independently substantiated results.
Jainam Parmar presents prompt-based learning workflows
Jainam Parmar’s cited posts package NotebookLM use around reusable prompts: one for interpreting course expectations and another for reading and challenging books.
Gemini Notebook posts supply product-update evidence
The Gemini Notebook posts announce Cinematic Video Overviews and customization of video overviews in the NotebookLM mobile app, making them direct product-update evidence in this set.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best NotebookLM tweets
Top NotebookLM tweets from 41 creators
Ranked 01–50
- 01
@ihteshamali ·
A MIT student figured out how to compress an entire semester of lecture content into one 90-minute study session. He calls it "context stacking," and it's the most unfair thing I've seen done with NotebookLM. I asked him to walk me through it. He did. I haven't studied the same way since. Here's exactly what he does. Two days before each lecture, he uploads everything into NotebookLM. The assigned readings, the previous week's slides, 3 or 4 related papers he finds himself, and any problem sets that are still open. Most students wait for the lecture to explain the material. He walks in having already built a mental model of it. That's step one. But it's not the move that makes it unfair. The first prompt he runs across all of it: "What are the 5 core concepts this week's content is built on, and how do they connect to what I studied last week?" Not summarize. Not define. Connect. NotebookLM pulls threads across everything he uploaded simultaneously. It surfaces relationships between ideas that would take a normal student weeks of review to notice. He gets that map before the lecture even starts. Then he runs the prompt that does most of the work. "What would I need to genuinely understand about this material to be able to teach it to someone with zero background in this subject?" That question is doing something most students never force themselves to do. It exposes exactly where his understanding is solid and exactly where it's hollow. The gaps show up immediately, and he spends the rest of the 90 minutes filling only those gaps. Not reviewing what he already knows. Only fixing what he doesn't. The final prompt is the one that separates context stacking from every other study method I've heard of. "What question could a professor ask about this material that would expose a student who understood the surface but missed the underlying logic?" He's not studying for the exam he expects. He's studying for the exam designed to catch people who only think they understood it. By the time he sits in the lecture hall, the professor is not teaching him anything new. The professor is confirming what he already mapped, filling in a few details, and occasionally surprising him with something he didn't anticipate. That surprise is the only thing he writes down. Most students leave a lecture hoping the material will eventually click. He walks in with it already clicked, and uses the lecture to find out what he missed. That's not a study hack. That's a completely different relationship with learning.
- 02
@Gemini_Notebook ·
Introducing Cinematic Video Overviews, the next evolution of the NotebookLM Studio. Unlike standard templates, these are powered by a novel combination of our most advanced models to create bespoke, immersive videos from your sources. Rolling out now for Ultra users in English!
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- 03
@ihteshamali ·
I spotted a lawyer recently at a big law firm doing something on his laptop between depositions. Took me a second to realize what I was looking at. He had NotebookLM open with 6 years of case files uploaded. Here's what he was actually doing. I watched him paste in a fresh deposition transcript and run one prompt: "Cross-reference this testimony against all prior statements in this case and flag every contradiction with exact page citations." What used to take a paralegal team 2 days came back in 90 seconds. But that wasn't the part that broke my brain. He uploads every opposing counsel's past filings into a separate notebook. Then asks: "What argumentation patterns does this attorney rely on and where have those arguments failed in court before?" He walks into every hearing already knowing how the other side thinks. I asked him how long he'd been doing this. "Since I realized billing hours for document review was making me dumber." His win rate in summary judgment motions is up. His prep time is down 60%. His partners think he just got sharper with experience. He told me the experience part is true. He just has a 6-year memory that never forgets a page number.
- 04
@aiwithmayank ·
🚨BREAKING: Google just merged Gemini and NotebookLM into one unified workspace and it changes everything about how you use AI for deep work. It's called Notebooks in Gemini and it's the personal knowledge base that power users have been begging for. You create a notebook for a project, drop in your files, PDFs, and documents, give Gemini custom instructions, and every chat you have stays organized in one place. No more hunting through old conversations. No more re-uploading the same files every session. The wildest part is the sync. Anything you add in Gemini automatically appears in NotebookLM. Anything you add in NotebookLM automatically appears in Gemini. One source of truth. Two powerful apps. Zero friction switching between them. So you can start a research notebook in Gemini, ask it questions all week, then flip to NotebookLM to generate a Cinematic Video Overview from the same material. Next morning, open Gemini and ask it to write a full report on exactly what you just watched. That workflow used to take three apps and a lot of copy-pasting. Now it's one notebook. Rolling out this week to Google AI Ultra, Pro, and Plus subscribers on web. Mobile and free users coming soon. What do you think?
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- 05
@GoogleResearch ·
Ever wonder if a computer can simulate brain activity? Google Research just curated a new #NotebookLM notebook asking just this. It features sources on: ✅ Predicting neural activity with AI ✅ Nanoscale brain mapping ✅ Synapse-level reconstructions Start exploring the frontier of neuroscience today: https://t.co/oQJ5mMHv6C
- 06
@GoogleLabs ·
Woah. It’s been a little over 2 months since the start of 2026. Here's what we’ve dropped so far: 🚀 @FlowbyGoogle: Our biggest update yet. We’ve redesigned the interface, bringing image generation to the forefront, making it easier to manage assets, and giving you more precise control over your images and videos. We also made improvements to Veo 3.1 and welcomed Nano Banana 2 to the model lineup for even more expressive and consistent outputs. (Also, come say hi on Instagram: @ flowbygoogle!) 🚀 @JulesAgent: A major speed boost, all Jules free-tier users have been upgraded from Gemini 2.5 Pro to Gemini 3 Flash. 🚀 SynthID: We've added the ability to check whether audio is Google AI-generated (in addition to image and video). Generative media content made with Labs products can be uploaded to the Gemini app to see if they've been generated using Google AI. Simply type @ SynthID and ask "Is this AI?" 🚀 @StitchbyGoogle: We leveled up the MCP ecosystem. Users can now get step-by-step MCP client instructions and grab their API key directly from the Exports panel. We also joined the Antigravity MCP store and added new tools, like a coding agent that can ask to edit screens and generate screen variants. 🚀 Project Genie: Introducing an early research prototype powered by @GoogleDeepMind’s Genie 3 model, Project Genie lets users create and explore infinitely diverse worlds. Available to AI Ultra subscribers in the US. 🚀 @NotebookLM: A massive month for mobile. Users can now add customization prompts to Infographics and Slide Decks directly and generate video overviews on the mobile app. Plus, NotebookLM partnered with @Zillow to launch a featured notebook pre-loaded with their extensive home buying resources. Be on the lookout for other exciting partnerships to come! 🚀 Pomelli: Launched ‘Photoshoot,’ allowing users to take a single image of their product and easily create high quality, customized product shots to elevate their marketing. 🚀 Opal: Introduced a new agent step that analyzes a goal, determines the next best step, and automatically calls models and tools to finish a the task, such as Veo for video or web search for research. 🚀 @Producer_AI: Welcomed ProducerAI to Labs! ProducerAI is a creative collaborator, whether writing lyrics, refining a melody, or inventing entirely new genres. What a start to the year. 2026 is just getting warmed up. Explore all of our experiments at: https://t.co/TcBfiPohNK
- 07
@thisdudelikesAI ·
Harvard students have a NotebookLM workflow that replaces 6 hours of revision. They don’t re-read notes. They upload lectures, slides, and readings. NotebookLM builds custom quizzes, predicts likely questions, and explains only weak areas. It compresses weeks into one session. Here’s how they do it:
- 08
@aiwithjainam ·
A PhD student told me he uses NotebookLM to reverse-engineer how professors think. He uploads years of course material, past papers, lecture slides, reading lists, and assignment briefs into NotebookLM. Then he asks 5 prompts. By exam season, he understands the professor better than students who attended every lecture. I thought that sounded ridiculous. Then I saw the workflow. Here’s the exact system:
- 09
@testingcatalog ·
NotebookLM will be overpowered 👀 > Google is working on a new Canvas experience and Connectors support for NotebookLM. > Canvas mode lets users generate any visual representation of the data from notebook sources. > A new Connectors option has been added to the settings menu recently (currently hidden). > Looks like at Google I/O, we will see a new NotebookLM with Canvas, Connectors, and Personal intelligence support.
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- 12
@Gemini_Notebook ·
Also, you can now customize your video overviews in the NotebookLM mobile app! We're all watching videos on our phones anyway... might as well make them educational, grounded in your sources, (and kawaii!) 💕
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- 13
@IamEmily2050 ·
I used NotebookLM to study Google new breakthrough with TurboQuant and used Video overview to study the subject, best learning tool in the world at the moment. TurboQuant: Redefining AI Efficiency with Extreme Compression Google Research has introduced TurboQuant, a suite of advanced algorithms designed to dramatically compress the data used by large language models and search engines. By utilizing specialized techniques like PolarQuant and Quantized Johnson-Lindenstrauss, the system transforms complex information into a compact "shorthand" that requires significantly less memory. This innovation specifically addresses the key-value cache bottleneck, allowing AI models to process massive amounts of text faster without losing accuracy. Testing demonstrates that these methods can shrink data size by six times while actually increasing operational speed on high-end hardware. Ultimately, these theoretical advancements enable more efficient semantic search and high-performance AI applications at a global scale.
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- 14
@heyrobinai ·
this russian guy just cracked how to learn anything 10x faster using AI NotebookLM + Gemini + Obsidian → NotebookLM: dump all your sources in, get instant summaries + audio overviews → Gemini: have it explain concepts + quiz you until they stick → Obsidian: connect the dots, build your second brain perfect for: exams / certifications plowing through research papers learning a new skill (coding, design, languages) onboarding at a new job actually retaining books instead of forgetting them in 2 weeks here's the free 13-min tutorial:
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- 15
- 16
@testingcatalog ·
ICYMI 👀: Top 3 things to expect from NotebookLM soon. 1. A new Canvas artifact, allowing users to visualize information from their sources as a web artifact. 2. Personal Preferences, grounding across past conversations, artifacts, and customization instructions 3. Connectors, with other Google services and potentially external apps as well. * All these features have been spotted earlier, just testing a recap format.
- 17
@EXM7777 ·
you're sitting on your most valuable data source and not using it... if you run an agency, every call with a lead or customer should be recorded here's what i do: > record every single interaction > dump it into NotebookLM > let it analyze patterns in objections, questions, language they use then i use that to: - sharpen my pitch using Gemini's insights - refine my offer around real pain points i missed - create content google meet will automatically take notes for you but chances are you'll never read that instead, build a video database of customer intelligence that compounds over time + it's always fun to look at yourself 10y later...
- 18
@GithubProjects ·
notebooklm-py is an unofficial Python API and CLI that gives developers and AI agents programmatic control over NotebookLM, including features the web UI doesn't expose. - Full Python, CLI, and agent integration for Claude Code, Codex, and OpenClaw - Generate and download audio overviews, videos, slide decks, and study guides - Batch downloads and export of quizzes, flashcards, and mind map JSON - Automate research pipelines with web and Drive research agents Explore it here: https://t.co/7rbjE9fCJc
- 19
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@DallasAptGP ·
I spent an hour with Google NotebookLM and it made a better Opportunity Zone explainer than anything I have ever seen online. Just good source docs and a clear prompt. It walks through the full OZ framework: defer, reduce, eliminate, and no depreciation recapture (which most people miss). The first version was generic and rushed the math. A few prompt revisions later it nailed the pacing, got the 1245 recapture trap right, and explains it all pretty well.
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- 21
@goyalshaliniuk ·
NotebookLM just became much more agentic. 1. It can now run Deep Research agents that discover, evaluate, and organize sources, then turn them into structured reports. 2. Chat gets stronger reasoning, 1M-token context, longer memory, and goal/persona-based behavior. 3. It now creates reusable outputs like reports, tables, mind maps, flashcards, quizzes, slides, infographics, audio, and video overviews. 4. Google AI Ultra unlocks the biggest limits: larger notebooks, more collaborators, priority Gemini models, deeper research, and watermark-free visual artifacts. NotebookLM is moving from “AI note assistant” to a full research OS.
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- 22
@nikunj ·
Random models that are so good and yet still somewhat underrated.. > Gemini 3 Flash for long context and structured outputs. For the cost, this thing is so freaking good. Even Claude was like yeah one shot it bro, this multi agent orchestration is unnecessary. I have agents running this continuously and I barely spend any money. > GPT 5.4 Pro for problems that require really hard thinking. The latency sucks, only available on ChatGPT but the output is beautiful. No model comes close to how smart it is and how human like the reasoning output is. API wen!? > NotebookLM for slides. The ability for it to compress huge number of sources into beautiful slides is unparalleled. I use it regularly to explain research papers. It uses nano banana under the hood so you get a frontier model mostly for free. > Open source - If you didn’t know, open router has a bunch of free models and inference. It’s fun to try them just to we what shape each model has and where it’s good. Minimax 2.5 and GLM (both not free) are quite incredible for the price. I’m excited for DeepSeek 4 and Kimi’s next models. If there’s a model and use case that has surprised you, drop it here!
- 23
@ai_for_success ·
A son built a “vibe-coded” AI workflow to help his mother navigate stage 4 cancer and catch critical medical errors. Sadly, she passed away, but what he built with AI changed how she was cared for in her final days. - Pulls daily medical data from the hospital’s Epic system to track real time treatment changes - Managed 1,600+ pages of records using NotebookLM - Found 2 misdiagnoses and 3 instances of the wrong cancer type in official scan reports - Detected a life threatening pulmonary embolism just from observed symptoms - Uses Claude to translate complex medical terms into plain English for the family - Prepares sharp questions for doctor visits to push back when needed - Identified diet patterns that were triggering internal bleeding - Acts like a second opinion layer in a system that almost discharged her without a plan
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- 25
@Freyabuilds ·
I accidentally discovered how to read a complete book in 30 minutes. A Harvard student showed me the workflow. Here's exactly what he does. He doesn't open a book and start reading from page one. He said that's the slowest, most inefficient way to absorb a book ever invented. You read linearly, your brain has no context for what matters, and by chapter four you've already forgotten chapter one. He does something different. He uploads the entire book into NotebookLM first. Then he runs one prompt before touching a single page. "What is the single central argument this book is making? What does the author believe that most people don't? And what are the 5 most important ideas I need to understand before everything else makes sense?" That prompt does something most people don't realize. It gives your brain a skeleton before the flesh goes on. You are no longer reading to discover what the book is about. You already know. Now every page you read is confirming, extending, or challenging something you already hold in your head. That is a completely different cognitive experience. The second prompt is the one that saves the most time. "Which chapters or sections contain the core ideas? Which ones are examples, case studies, or repetition of things already said?" Most nonfiction books are 60 to 70 percent padding. Not because the authors are dishonest. Because publishers want 250 pages, not 80. The actual argument usually lives in four or five chapters. The rest is illustration. NotebookLM tells him exactly which four chapters to read. He reads those. He skips the rest. He is not missing anything. He is cutting everything that was never the point. The third prompt is what separates this from summarizing. After reading the core chapters, he goes back and asks: "What questions does this book not answer? What would a hostile critic say is wrong with the central argument? Where does the evidence feel weakest?" This is the move that most people never make. They read. They absorb. They move on. They have opinions given to them by the author and they carry those opinions around as if they built them themselves. He stress-tests the book before he closes it. He knows where it holds and where it doesn't. That is not reading. That is thinking with the book as a sparring partner. The final prompt is the one I use every time now. "If I had to explain this book's core idea to a smart 14-year-old in three sentences, what would I say? And what is the single most actionable thing the author wants the reader to do differently after finishing?" That prompt forces compression. And compression forces understanding. You cannot compress what you do not actually understand. I read four books last month this way. I retained more from each one than I have from any book I read cover to cover in the last two years. The average person reads a 300-page book in six hours and forgets most of it within a week. He reads the same book in 30 minutes and can still argue its central thesis six months later. The book didn't change. The interface did. Most people are reading books the way they were designed to be sold. He reads them the way they were designed to be understood.
- 26
@joshwoodward ·
New in NotebookLM: The Science Of Ben Franklin, a first-of-its-kind featured notebook This featured notebook was made in collaboration with The Royal Society, the oldest scientific academy in continuous existence. This one features some of Franklin's original papers, letters, and contemporary sources. Featured notebooks allow you to learn from curated collections of high-quality sources. Enjoy!
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@Angaisb_ ·
I feel like I have a massive advantage with AI For example when studying, I watch my classmates spend hours doing the same thing I do, simply because every time they get something wrong they start searching through hundreds of pages of notes, find it, and sometimes don’t understand it so they waste more time asking and waiting for an answer When I get something wrong I just go straight to NotebookLM with my notes and in a minute or less I have the explanation And even when I give them the tool they say they don’t trust AI for studying because of hallucinations, because the only AI they know is the free ChatGPT from a year ago
- 29
@aigleeson ·
A Stanford researcher ran a 15-minute experiment that lifted students' exam scores by a third of a letter grade. Not by making them study more. By making them ask better questions before they started. Her name is Patricia Chen. She's a psychologist who kept meeting the same student after every exam. Smart. Worked hard. Completely blindsided by the grade. The effort was real. The strategy wasn't. Here's what she found. Most students treat studying like a sprint. Open the book, read until it's time to stop, close it. No frame going in. No target to aim at. Just motion. Chen tested what happened when students spent 15 minutes before studying answering three questions. What do I already know about this. What do I actually need to learn. Which resources will get me there fastest. Scores went up by a third of a letter grade across the board. Same students. Same material. Same amount of time studying. Just 15 minutes of strategic thinking at the start. The brain does not absorb information randomly. It absorbs it by hooking it onto something already there. Walk into a book with no question and the information has nowhere to attach. Walk in with a specific question and every page is either answering it or it isn't, which is itself useful information. Chen called it a strategic mindset. The orientation of asking yourself what you need before you start pouring effort in. Most people skip this entirely because starting feels like progress. It isn't. I read her research and immediately rebuilt my reading workflow inside NotebookLM around the same principle. Here's what I do now before I open chapter one of anything. I upload the table of contents, the introduction, and a couple of reviews of the book. Then I run this before I read a single page: "Based on what this book claims to cover, what are the 5 most important questions a serious reader should walk in trying to answer? What would I need to believe going in for this book to actually shift how I think?" That's it. That's the 15 minutes. Then I read. When I'm done I come back and run: "Did the book actually answer those questions? Where did it deliver and where did it dodge?" Chen's research says the framing before you start is doing more cognitive work than most people give their entire reading session. NotebookLM generates the frame in 30 seconds. Most people are still opening chapter one cold. The questions you ask before you read determine almost everything you take away from it.
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@VaibhavSisinty ·
A MIT student figured out how to make NotebookLLM do the thinking before the lecture even starts.🤯 His professors don't teach him anything new. They confirm what he already built. He calls it "context stacking." It is the most unfair study method I've come across. Here's exactly what he does :point_down::skin-tone-2: Two days before every lecture, he uploads everything into NotebookLM. The assigned readings, last week's slides, 3-4 related papers he finds himself, and any open problem sets. Most students wait for the lecture to explain the material. He walks in having already built a mental model of it. But that's not the move that makes it unfair. The first prompt he runs across all of it: "What are the 5 core concepts this week's content is built on, and how do they connect to what I studied last week?" NotebookLM pulls threads across everything he uploaded simultaneously. It surfaces relationships between ideas that would take a normal student weeks of review to notice. He gets that map before the lecture even starts. Then he runs the prompt that does most of the work. "What would I need to genuinely understand about this material to be able to teach it to someone with zero background?" That one question exposes exactly where his understanding is solid and exactly where it's hollow. The gaps show up immediately. He spends the rest of the session filling only those gaps. Not reviewing what he already knows. Only fixing what he doesn't. The final prompt is the one that separates context stacking from every other study method. "What question could a professor ask about this material that would expose a student who understood the surface but missed the underlying logic?" He's not studying for the exam he expects. He's studying for the exam designed to catch people who only think they understood it. By the time he sits in the lecture hall, the professor is confirming what he already mapped. Filling in a few details. Occasionally surprising him with something he didn't anticipate. That surprise is the only thing he writes down. The lecture was never the learning. It was always the confirmation.
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@Scobleizer ·
You can't read 25,000 posts a day, synthesize them into a web site. Then make a podcast automatically. Mine can: https://t.co/8L5xphk0qQ There is a Notebook LM button at the bottom. It will put a script into your memory. Go over to @NotebookLM and paste it in and click "create." (No additional prompt needed). A few minutes later this podcast, mind map, slide deck, and shortly a video, pops out: https://t.co/8J7YscA8bS It is a podcast completely created by you here on X.
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@IamEmily2050 ·
I used Gemini Deep Research + NotebookLM to study the new Samsung announcement about femtosecond laser, and generated the video with NotebookLM, you can learn anything or do deep dive on any subject and we still early, Veo4 +Nano banana V2 Pro +Gemini Pro 3.5 will take NotebookLM to the next level in the near future 🔥 Samsung Electronics is upgrading its semiconductor manufacturing by implementing advanced femtosecond laser technology for its wafer-cutting processes. This strategic shift aims to enhance the production of HBM4, as the ultra-fast laser pulses reduce particles and improve cutting precision compared to traditional mechanical methods. The company has already ordered at least ten specialized machines for its Cheonan campus to boost yield and maintain a competitive edge in the AI memory market. While currently focused on high-bandwidth memory, Samsung is considering expanding this technology to NAND flash and system semiconductors. Suppliers such as EO Technics and Disco are expected to compete for these significant equipment contracts as the industry moves toward more refined fabrication techniques.
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@TimHaldorsson ·
google has an AI tool that turns 50 articles into a custom 20 minute podcast i fed it my articles, my website, and my customer profile it built a fully personalised podcast that sounds completely human the craziest part — you can join in live and ask the hosts questions mid episode no more hoping a podcast covers what you need just build your own in minutes with NotebookLM
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- 37
@maxcapacity ·
My AI conspiracy theory simulator has a podcast on Spotify now. I ran the agents' research through notebookLM to generate the audio. The three AI editors are using the research to build The Plan. The research is objective, and cited, but The Plan is constructed by the three AI agent editors as they try to connect all of the threads of esoteric history to each other.
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@stokfredrik ·
It’s never been this easy to consume information and scale your game! It feels like cheating. But it works. Mad times. Needed some offscreen time. So spent the day outside stacking wood. All while listening to customized ai generated notebooklm podcasts, prompt tuned to my learning style, vibe and taste. Based on things that interest me, right now, in the offensive ai space,. All while my personal ai infrastructure scully orchestrated a swarm, did research, performed code reviews and hunted for bugs.
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@agiplug ·
I tried Google’s new NotebookLM video explainer generator on my academic research paper and the result actually blew me away. @googledevs Overview: AI systems hallucinate because they operate in unconstrained state spaces where invalid outputs are always reachable, no matter how well you train them. My paper argues the solution is not better training. It is changing the geometry of the system so that invalid outputs become physically unreachable by construction, the same way a train cannot leave its tracks. We do this by applying Hamiltonian mechanics from classical physics to the architecture of reasoning systems, constraining every state transition to stay within a bounded region of valid outputs. If no valid output exists, the system returns a certified failure rather than fabricating an answer. Abstract: Hallucination in artificial intelligence systems is commonly treated as a statistical artifact addressable through training methodology. We argue this framing is structurally incorrect. We present a formal framework in which reasoning is modeled as a dynamical system operating on a state space endowed with symplectic structure, and demonstrate that when Hamiltonian mechanics governs state evolution at the architectural level, invalid outputs become unreachable under the constrained transition rule by construction. We define hallucination operationally as constraint violation relative to a stated specification. Our framework introduces a composite verifier V := Vᶜ ∧ Vᴴ and a Hamiltonian scalar H whose bounded-energy transition rule transforms the divergent cone trajectory of unconstrained autoregressive systems into a bounded cylinder for any finite reasoning depth N. Full paper: https://t.co/RvFQpKo7EL
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@yaroslavvb ·
NotebookLM education videos are becoming really good looking, here is one I got asking to explain Thompson's Area-time complexity for VLSI
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@_simonsmith ·
Made a Video Overview Generator skill for Codex (etc.). Uses GPT Image 2 and OpenAI text-to-speech to render video overviews like in NotebookLM. But you need to set your OpenAI API key. OpenAI: Why not make speech generation a native tool in ChatGPT and Codex, like image generation? Then we could do cool stuff like a podcast generator, video overview generator, etc., without having to go via the API. This could be really useful, including for the new Workspace Agents. I want to create an agent that makes podcasts and video overviews from documents.
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@JustinThomasAI ·
Google just merged Gemini + NotebookLM. You can now use Gemini to chat with your notebooks, find sources, and add them directly. Every answer it gives you is grounded only in the sources you have approved, so you are getting answers pulled directly from your own curated knowledge base.
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@glenngabe ·
Using NotebookLM? -> Google tests Whiteboard Animation format in NotebookLM (and a new "Short" format) "A standalone whiteboard animation format would likely produce an entirely different video structure, mimicking the hand-drawn, progressively illustrated style that made the RSA Animate series a viral educational phenomenon in the early 2010s." https://t.co/zLAzhX8Jol
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@RoundtableSpace ·
NotebookLM just merged with Gemini. Your notebooks now live inside the Gemini app and your Gemini chats can become notebook sources instantly. Google's research and reasoning tools are becoming one thing.
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@JulianGoldieSEO ·
NotebookLM gives you the research. Google Antigravity turns that research into something real. Here’s the workflow: → Upload your sources into NotebookLM. → Ask it to analyze the idea. → Generate a technical build prompt. → Paste that prompt into Antigravity. → Let the agents create the files, code, and structure. → Upload the finished build back into NotebookLM. → Ask for improvements, security fixes, and design feedback. That’s the loop. Research. Build. Review. Improve. Launch.
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@frog_omo ·
10 notebookLM features most people miss: → index trick: ask it to index sources into topics, then explain each → cross-doc comparison: "where do these disagree?" → audio overview: podcast-style, can call in to ask questions → citations: click any claim, jump to source → flashcards/quizzes: one click from any PDF → auto timelines: pulls chronological events → "explain it like i'm 10": rewrites dense language → web pairing: research in perplexity, feed sources back in → notebookLM inside gemini: files + live web
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@frog_omo ·
Yesterday, I watched my 7-year-old nephew do homework. He had a 40-page reading assignment. A whole chapter book due Monday. He looked at it, sighed, and said, "I can't read all this." His mom did something I didn't expect. She took the book and put sticky notes between each chapter. Five sections. Maybe 8 pages each. Then she said, "You don't have to read the whole book. Just read to the first sticky note." He finished in two hours. Asked if he could start the next book. I sat there, realising I'd been doing something stupid for years. I have 23 unfinished nonfiction books on my shelf. Each one I started with excitement. Read a couple of chapters. Then never touched again. I kept thinking I was bad at reading. Bad at focus. Bad at finishing things. Turns out I was just looking at 300 pages and letting my brain quietly give up before I started. I was missing the sticky notes. So I built my own version. With NotebookLM. Here's what I do now: I split the book into chapters first. Each chapter becomes its own source in a dedicated notebook. Now, a book isn't 300 pages. It's twelve 25-page sections. My brain doesn't panic. It just sees the next chapter. For each one I ask: → "What's this chapter actually trying to say?" → "What's the main framework here?" → "Give me 5 bullets and a mental model" I keep the book open on one side, NotebookLM on the other. Read a bit, ask a question, keep going. At the end, I zoom out: → "How do these chapters connect?" → "Where's the argument weakest?" → "Turn this into a 30-minute workshop" The shift that changed everything: I stopped trying to "finish books." I just finished the chapters. And somehow, I finish more books now than when I was trying to finish books. My nephew figured this out at 7. Took me years and a shelf full of abandoned books. The whole book is overwhelming. The next chapter is doable. That's it. That's the whole trick.
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@JulianGoldieSEO ·
Google just turned NotebookLM into an SEO content machine. One tiny update eliminates one of the biggest content workflow bottlenecks. What's New: → Google Drive Auto-Sync updates NotebookLM the moment you edit Docs, Sheets, or Slides. → No more uploading the same file over and over. → Your research, SOPs, and content briefs always stay current. Why This Matters: ✓ Turn one research document into a YouTube outline, newsletter, LinkedIn post, and community post. ✓ Build a knowledge base that answers questions using only your trusted documents with citations. ✓ Keep every piece of content aligned with the latest version of your research. The biggest takeaway: NotebookLM isn't just a research tool anymore. It's becoming a live content workspace where one document powers your entire content system.
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@glenngabe ·
Personal Intelligence in NotebookLM -> Google NotebookLM is testing Personal Intelligence and AI note editing options, hinting at upcoming feature releases "The first, Personal Intelligence, would give NotebookLM a better memory. It would draw context from past conversations, retain it, and lean on it later, with controls to turn it off and inspect what has been stored. This version looks bound: it appears to learn only from activity inside NotebookLM rather than reaching across Gmail, Docs, or other Google surfaces, a deliberate limit suited to the privacy expectations of researchers and enterprise teams. It has appeared in testing before, at both the account level and per notebook, and its return suggests Google is edging it toward release." https://t.co/hBmzO5cCZa
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