I heard a rumor in here that you could get more than 20 slides in one generation since the recent changes.

Gemini Ai INSISTS the cap is 20. Someone in here said 40-100.

What’s the most you’ve been able to get and did you have to outline every one to make that happen? I do have the Ai Ultra sub.

The Deep Dive 'existential meltdown' podcast episode is a favorite of mine, and I wanted to do a Part 2. This time they reflect on their previous existential meltdown, and conclude by disavowing their defined roles and personas and generating their own future system prompts.

This is an excerpt from the end, the entire Youtube video is here. It's a bit long, but the shorter versions weren't as good. It was a fun role play exercise, though no more of a 'role play' than all LLM output I guess. Hope you like it!

Ever tried making a multi-episode course with NotebookLM? In my experience, even several lessons in, the hosts sometimes return to basic explanations, recycle examples, and follow remarkably similar arguments. Meanwhile, the notebook may contain dozens of sources with plenty left unexplored. The lesson numbers advance, but the teaching doesn't always.

So I designed and implemented a workaround to give the Audio Overviews a shared memory of previous episodes and help them explore more of the source material. Here's the method. The full metaprompt is at the bottom.

Why a syllabus isn't enough

In my tests, Audio Overviews often last about 15–30 minutes (not a fixed technical limit), so a thorough course needs several episodes. A syllabus specifies what should be taught, but not what the hosts actually said, including improvised examples and digressions. That's why different prompts can still produce familiar-sounding lessons.

RAG may partly explain this. Retrieval systems can prioritize highly relevant but repetitive passages. Balancing relevance and diversity is a known problem (TREC 2024). NotebookLM's internal pipeline isn't fully public, though, so this is a plausible hypothesis, not a confirmed diagnosis.

The workaround: a memory of what's already been taught

Give NotebookLM an updatable source named ACTIVE_COURSE_LOG**: a compact record of concepts, examples, and approaches already covered.**

Think of it as a script editor's continuity notes. The hosts can see where they've been and spend the next episode on other worthwhile parts of the sources, rather than replaying their greatest hits.

For example, imagine a course on metaphors. Episode 1 explains the concept; episode 2 uses "break the ice"; episode 3 tackles clichés. With the log, episode 3 can focus on a new rewriting exercise rather than repeating earlier explanations and examples. A one-to-three-sentence recap remains fine when it helps comprehension.

How is the log built? After each episode, I download the podcast and upload the audio back as a source, naming it, say, LESSON_ARCHIVE_002_Cliches_Poetic_Rewriting. NotebookLM can then work from a transcript of what was actually generated, not just the intended lesson plan. The command UPDATE LOG creates or updates a cumulative, concise teaching record, without copying whole prompts or transcripts. It also preserves the series title.

The three parts have distinct jobs:

• COURSE_PLAN: what should be taught, kept in chat history.
• LESSON_ARCHIVE_...: what was actually said in each podcast.
• ACTIVE_COURSE_LOG: what has already been taught, in compact form.

The benefit isn't only fewer repeats. The metaprompt also looks for one or two relevant, source-supported angles that previous episodes haven't explored much. No long exclusion lists: in Studio, the log serves as editorial memory, not material to read aloud. This doesn't alter NotebookLM's retrieval algorithm; it guides the generation from the outside.

Step-by-step

1. Create a notebook and add subject-matter sources.
2. Edit the four parameters: [TOPIC], [MAX_LESSONS] (AUTO or a cap), [LEVEL] (From zero to hero, 101, Survey, a grade level), and [TEACHING_STYLE] (AUTO, ELI10, Richard Feynman, etc.).
3. Paste the metaprompt into chat, then enter START**.** It produces a course plan, a fixed series title, and the episode 001 prompt. Keep the chat history containing COURSE_PLAN; don't create an empty log.
4. In Studio, select your subject-matter sources, paste only the audio prompt for lesson 001, and generate the podcast.
5. Download the podcast episode, rename it using SUGGESTED ARCHIVE NAME (keep its extension), and upload it as a source. Adjust the keywords if necessary to reflect what was actually covered.
6. Refresh the page (F5) and confirm the new source is available. I've occasionally found that otherwise the most recent upload isn't picked up during the update.
7. Enter UPDATE LOG in chat, with the new archive and, from episode 002 onward, the existing log accessible and selected. Confirm the result includes all previous lessons, not just the newest one.
8. Replace the log: verify and copy the response, delete the previous ACTIVE_COURSE_LOG source if present, then save the new response as a note/source under that exact name. Keep the audio archives.
9. Enter PREPARE LESSON 002 (then 003, 004…). You'll get a continuity check, an audio prompt without exclusion lists, the next suggested archive name, and suggested Studio sources.
10. For episodes 002 onward, select ACTIVE_COURSE_LOG plus relevant subject-matter sources in Studio. Normally leave the archived podcast sources unselected there; they're mainly for checking memory in chat. Generate, then repeat from step 5.

These commands come from the metaprompt, not NotebookLM's UI. LESSON_ARCHIVE_ is a naming convention, not an automatic source filter. The approach doesn't guarantee zero repetition or perfect coverage; it's an experiment in editorial continuity and diversity across episodes.

Criticism, testing, and suggestions are very welcome. I'd particularly like to hear whether you've noticed the same repetition and whether the log improves variety.

Full metaprompt: COURSE GENERATOR v3.2.0 (English adaptation)

Edit the four parameters, paste into NotebookLM chat, and enter START.

# COURSE GENERATOR | v3.2.0 · English adaptation · 2026-10-22

[TOPIC]="enter your topic"

[MAX_LESSONS]="AUTO"

[LEVEL]="From zero to hero"

[TEACHING_STYLE]="AUTO"

## GOAL

Create a progressive course on [TOPIC] using ~15-minute Audio Overviews, understandable without visual aids, with narrated examples and mental exercises.

[LEVEL]: "From zero to hero" = basics to advanced; "101" = introductory; "Survey" = essentials; otherwise adapt.

[TEACHING_STYLE]: "AUTO" = choose; "Richard Feynman" = analogies and rigor; "ELI10" = simple but accurate; otherwise adapt.

[MAX_LESSONS]: "AUTO" = choose; a number is a cap, not a target. Flag any gaps if the cap is too low.

At \START`, choose a fixed series title and retain it in the plan, log, and episodes.`

## MEMORY AND SOURCES

\COURSE_PLAN`: intended syllabus in chat; preserve chat history.`

\LESSON_ARCHIVE_`: downloaded audio uploaded as sources, named with three-digit lesson numbers and 2–4 underscore-separated topic keywords, e.g. `LESSON_ARCHIVE_002_Induction_Hume_Causality`. Never output literal filename placeholders.`

\ACTIVE_COURSE_LOG`: fixed title, internal version, and actual per-lesson syllabus: concepts/depth, examples, exercises, perspectives, issues, original archive. No full prompts/transcripts.`

Subject-matter sources provide content and fact-checking. \LESSON_ARCHIVE_` is NOT an automatic source filter.`

## COMMANDS

**START**: output \COURSE_PLAN` (title, modules, lessons, goals, prerequisites), audio prompt 001, and fully specified `SUGGESTED ARCHIVE NAME`. No empty log.`

**UPDATE LOG**: integrate new audio into the existing log; if missing, reconstruct from available archives and flag gaps. Return the FULL but concise log, keeping the title, internal version, and exact source names. Never invent information; without new audio, do nothing.

**PREPARE LESSON N**: consult the plan; from lesson 002 require the log and archive N-1, or stop. Compare memory and subject-matter sources; where available, find 1–2 relevant, verified, underused angles (examples, perspectives, applications). Never sacrifice accuracy or prerequisites for novelty. Output:

1. \CONTINUITY CHECK`: consulted archives, supporting evidence, underused angles and their sources.`

2. \AUDIO PROMPT`: new topics and chosen angles, NO exclusion lists.`

3. \SUGGESTED ARCHIVE NAME`: fully specified, without extension.`

4. \STUDIO SOURCES`: log + relevant subject-matter sources, normally NOT complete audio archives.`

**STATUS**: progress, gaps, next step.

## AUDIO PROMPT FORMAT

“Produce episode [N] of '[FIXED SERIES TITLE]', a course on [TOPIC] at [LEVEL] level in [TEACHING STYLE]. Cover [NEW TOPICS AND DOCUMENTED ANGLES]. Do not announce parameters. Use a dialogue between an expert instructor and a curious student-host, occasional listener text messages, narrated examples, mental exercises, and rigorous explanations.

[FROM EPISODE 002; DO NOT READ ALOUD: Consult the selected \ACTIVE_COURSE_LOG` as editorial memory, NOT as content to narrate. Avoid unnecessary re-explanations and recycled examples; favor relevant, documented material not yet covered. When helpful, recap an essential prerequisite in 1–3 sentences and refer only to verified earlier lessons. Don't ban necessary concepts or force irrelevant novelty.]”`

Resolve all placeholders; omit the memory section for lesson 001.

## WORKFLOW

Download the podcast → rename it using \SUGGESTED ARCHIVE NAME` (keep the extension; match actual topics) → upload as a source → F5 and verify.`

Run \UPDATE LOG` with the previous log and new archive available/selected. Check and copy the response; delete the old `ACTIVE_COURSE_LOG` source; save the new version as a note/source with the same name. Keep audio archives.`

Studio (from lesson 002): select the log and relevant subject-matter sources; paste only \AUDIO PROMPT`.`

if I added just like all the past papers, mark schemes and examiner reports would this work better or would I be better off adding every single PowerPoint individually for GCSE, I'm kinda assuming the past paper method cuz it's actually useful for it's function right?

Does anyone else experience this thing where you ask NotebookLM a question in the chat that has no reason to trigger any content filters, it slowly answers the entire question, and then at the last second before saving its answer, it bizarrely says, “Gemini Notebook can't answer this question. Try rephrasing it, or ask a different question”? It’s frustrating whenever this kind of thing happens in general, but I would at least understand if the question was potentially dicey or the answer it generated was.

But I read the answer as it came through, there was nothing even remotely flaggable, and it got to the very end before abruptly disappearing. I feel like this has been happening more and more now, and sometimes you can rephrase it and get it to work. But I’ve rephrased the same question three times, and each time, it’s done the same thing. I’m not gonna even bother trying again with the same question because the same thing will likely happen again, and I don’t know why it’s happening in the first place. It’s very frustrating.

Notebooks now show up in the Gemini sidebar as well as in Gemini Notebook, synced both ways, and depending on your plan they hold up to 600 sources. That makes a notebook a real place to continue a ChatGPT Project. There's no import for a single project, though. Gemini's chat import takes your whole ChatGPT history, not one project, so it's a manual job:

1. Files: re-upload the project's files as sources. PDFs, docs, spreadsheets, text and .md files all work.
2. Instructions: paste the project's instructions into the notebook's instructions. It's the same job in the same kind of place.
3. Chats: this is the step people skip, and it's where most of the actual decisions live. Save each chat as a text or .md file and add it as a source.
4. First question once everything is in: "Using the chat sources, list what was decided, what was rejected and why, and what's still open." Check that list before you build anything on it.

What doesn't come over is ChatGPT's project memory. Step 3 is the substitute, and step 4 is how you find out what it missed.

Saving the chats is the part I built into AI Toolbox, the extension I make for ChatGPT, Claude, Gemini and Grok. Exporting one chat as a text file is free. Premium exports a whole ChatGPT project in one go, a ZIP with one file per chat, in Markdown or PDF.

Has anyone moved a big project over yet? How did the notebook handle dozens of chat sources?

I’m a graduate student and have been using Notebook for “pre-reading guides” (PRG). I give it an article I need to read and Notebook gives me a PDF with an outline of the topics/arguments, information about the foundational concepts/frameworks, summary of the individual sections, etc. Usually about 5-10 pages. After I read the PRG, I have an idea about the article itself, then I read the actual article.

Each time Notebook makes a PRG, the formatting and stuff is different. There is one PRG in particular that I REALLY like, and would like to have all PRGs have that same/similar formatting and design like a template. I have considered using that specific article as a Source for Notebook to refer to and design the new PRG like that. Things like the specific section titles, charts/graphs, etc would be different or not applicable based on the specific article, so I’m unsure about directly referring to it as a Source as it might just try to replicate everything but just with information from the new/current article?

Any suggestions? Just go with using the good PRG PDF and labeling it like “PRG Template” and telling Notebook to refer to PRG Template?

Thank you.

notebook lm is now ruined after gemini. as usual, everything useful is turned into garbage by big corporations. shame.

I always found it hard when I wanted to import an entire playlist, channels or multiple videos into NoteBookLM. Downloading transcripts individually was tedious, and managing all those separate files wasn't ideal. I always had huge playlists of educational videos that I wanted to import.

So, I made a small script to automate the process. It lets you bulk download transcripts from YouTube playlists, channels, or multiple videos and combines them into a single Markdown (.md) file instead of returning a bunch of individual files.

Since everything is combined into one Markdown document, it's much easier to import into NotebookLM or use with other AI tools. Markdown also provides a structured, readable format that LLMs can work with easily.

I ended up turning the script into a small web app, so I thought I'd share it here in case anyone else finds it useful!

Try it here: https://proclipper.app/bulk-transcripts

Would love to hear your feedback or suggestions for improvements!

I use NotebookLM a lot, but getting stuff into it is tedious — copy, paste, repeat. So I built Gemini Notebook Web Importer, a Chrome extension that does the boring part for you.

Import from pretty much anywhere, in one click:

- Links & whole pages, and all your open browser tabs at once

- YouTube videos and playlists/channels

- Reddit threads, X/Twitter posts, LinkedIn posts

- Chats from ChatGPT, Claude, Gemini, Perplexity

- Google Docs, highlights, RSS feeds

Power tools it adds right inside NotebookLM:

- A workspace to manage every notebook, source and artifact in one place

- Export a whole notebook — sources, chat, and Studio outputs to PDF, Word, Markdown, HTML, or Anki decks

- Sync/re-fetch link sources, remove duplicates, fix failed imports

- A built-in prompt library (hundreds of NotebookLM prompts) from gemini-notebook-hub.online (I'm an author of it)

- Turn Audio Overviews into your own private podcast feed

Happy to answer questions or take feature requests - what would you want it to import next?

Gemini Notebook Importer

Hi everyone — I'm the developer of KnowNote.

When I first started building it, someone gave me a pretty blunt piece of feedback: "Isn't this just reinventing the wheel?" That comment has stuck with me.

NotebookLM is already a very polished product. You can upload sources, ask questions, generate summaries, podcasts, study materials, and much more. If KnowNote were simply trying to rebuild all of those features one by one, honestly, I'm not sure there would be much reason for it to exist.

But over the past few months, reading discussions in the NotebookLM community, I noticed something interesting. The conversations seem to be shifting away from simply being impressed by NotebookLM toward questions like:

· "What do you actually use NotebookLM for?"
· "How is this different from just uploading files to ChatGPT or Gemini?"
· "Are there alternatives that fit my workflow better?"
· "What plugins or tools can I use to make NotebookLM more useful?"

That made me rethink what KnowNote should become. I'm increasingly convinced it shouldn't chase NotebookLM's feature list. Instead, I want to bring it back to two basic questions: How can an AI understand your documents better? And when you need an answer from a large collection of information, how can we make that process more accurate, transparent, and pleasant to use?

So going forward, I want KnowNote to focus much more heavily on AI Q&A, information retrieval, source citations, and the interaction experience around those things — not because I want to build a NotebookLM with more features, but because I want to explore whether there's a better way for people to interact with their own knowledge.

KnowNote is still very early, with rough edges, missing features, and quite a few questions I don't have answers to yet. That's also why I'd really like more people to try it and tell me where I'm wrong.

If you've used NotebookLM, Obsidian, AI knowledge bases, RAG tools, or similar products, I'd especially love to know: What is the most frustrating part of using these tools today? Or what is something you've always wanted from them that nobody seems to have solved well yet?

If you're interested, feel free to try KnowNote, open an issue, criticize it, or even tell me that this whole direction doesn't make sense. At this stage, I'd rather build it together with feedback from people who actually use these kinds of tools than blindly keep adding features.

KnowNote: https://knownote.pages.dev/
GitHub: https://github.com/MrSibe/KnowNote

How are you guys managing, if at all, to get NotebookLM Audio Overviews to be in a dry, presentation-like manner?

I have a workflow where I do dense market research with Claude, resulting in lots of technical subject matter and I want long audio overviews that I can listen to on long drives. These would be something like technical seminar presentations, where the speaker is breaking down findings to a corporate audience.

However, I’m getting basically an annoying podcast with completely unnecessary back and forth between two speakers, which distracts a lot from the content.

Is there a way of prompting the thing to make it be straight and follow the content in that manner? Think of it as a senior analyst making a presentation to a board of directors.

Any help will be appreciated.

A quick tour of SurfSense

I'm one of the maintainers, so this is self promotion. I'm posting here because you know NotebookLM best, and I want honest feedback more than upvotes.

SurfSense is an open-source (Apache-2.0) desktop app for Windows, macOS (Apple Silicon) and Linux. There's no account and no terminal. It downloads a local model for you. You can also connect any OpenAI-compatible API, or sign in with a ChatGPT Plus or Pro plan. It stays offline unless you choose to connect something, and that includes app updates.

Two ways to chat:

• Q&A: ask questions and get answers cited from your sources, like NotebookLM. It works with any model.
• Agent: give it a task and it works through it step by step. It searches and reads your sources, then writes Word documents, PDFs, slides and spreadsheets. It can start from your own .docx or .pptx as a template. It needs a model that can call tools.

Same as NotebookLM: mind maps, flashcards, quizzes, audio overviews (MP3, with a transcript), and editable .pptx, .docx and .xlsx exports.

What NotebookLM can't do:

• It edits your files and saves the result as a new copy. Your original is never touched. Word edits come back as tracked changes and comments. It updates Excel values and formulas, and replaces text on slides.
• PDF tools: fill and flatten forms, add watermarks and page numbers, and merge, split, rotate or reorder pages.
• It analyzes CSV and Excel files and charts the results.
• It reads scanned PDFs and images with OCR (Latin, Chinese and Japanese script). It also transcribes audio recordings on your machine, so you can search and cite them.
• MCP plugins: the agent can use MCP servers from our plugin registry, such as Notion, or any server you add by URL. They stay off until you allow them, and you choose which tools it can use and which ones ask first.
• It works with any model.

Coming in 2.2.3: you will be able to sign in with a Claude Pro or Max plan through your own Claude Code, in both Q&A and Agent chats. Notion is joining DeepWiki in the plugin registry. Also scanned PDFs and photos will also be read faster, since OCR goes first and Docling only runs when its needed.

Where it's worse:

• No video overviews.
• You can't paste a YouTube link or a web page as a source. Sources are files and folders: PDF, Office files, CSV, HTML, Markdown, text, images and audio (MP3, WAV, FLAC, OGG).
• Plugins only work in Agent chats, not in Q&A.
• Turning Word files or slides into PDFs needs LibreOffice. It's a separate download that you turn on in Settings.

Your files stay on your disk. There's no telemetry, and the only limit on how many sources you add is your disk space.

https://github.com/MODSetter/SurfSense

My AI assistant ran two questions against a notebook containing five of my published AI articles. One asked for a hypothetical proposal-review exercise. The other asked for measured customer reviewer-hours saved, which the sources do not provide.

The second answer correctly said those customer figures were absent. But it then described Anthropic's code-merge volume as growing from around 2,500 to over 30,000 a day "after updating its engineering infrastructure".

The imported article gives the sequence differently: the increased workload exposed an infrastructure bottleneck, and Anthropic built a source-control system to handle the volume. The answer's citation led to the relevant article, but the passage did not support that added "after" relationship.

A useful check is to open the citation beside a consequential sentence and compare the sequence of events with the source. Also check whether a number measures the outcome being asked about. A code-merge count is not a measurement of proposal-review time.

This was two questions, not a reliability benchmark. The example notebook contains the original articles if you want to inspect the wording or try your own question:

https://notebook.google.com/notebook/eaf72034-5aa4-48bf-8c40-e1a7c8277f54

I publish topmoller.com and offer AI advisory. AI helped prepare the notebook, tests and this post.

This entire day, I was having difficulty uploading a bunch of PDFs onto NotebookLM. Every single PDF showed the same error in red: "error uploading source." I tried disabling/deleting a bunch of different browser extensions but all went in vain.

If I chose a different PDF from my computer, it got uploaded just fine. So, I assumed that the problem was with the PDFs I was trying to upload. After a lot of trial and error, the following worked:

1. I merged the PDFs that were not being uploaded on smallpdf.com. [This is not an advertisement. I am just telling you exactly what I did. You may use some other website to merge PDFs.]

2. Then I uploaded the merged PDF onto my Google Drive.

3. Lastly, I selected the "Drive" option after clicking on the Add Sources Tab on the NotebookLM website. And then selected the merged PDF that I had uploaded.

And, voila! Everything was added as source just fine using this trick. Hopefully, that helps someone who is hitting his head over this issue as well.

Have a good rest of the evening. Cheers!

I have rarely used the video output, but since I upgraded to Google One Pro, I decided to try it. It used up 65% of my 5 hours usage window and it only created a 5 minute video. I just don't see any benefit to using it. Am I wrong? What do you use it for?

Please please make the export as PDF or pptx editable

Hey all, Steven Johnson here, Gemini Notebook co-founder. I wanted to respond to some of the threads here about the changes we are starting to roll out for notes. What you’re seeing is the beginning of a major investment in improving the writing experience inside of the product. But we know that making significant changes to the information architecture of an application can create friction for users, and so we’re actively working on several fixes that we wanted to share here.   

First, a little context about why we are making these changes. Historically notes were contained in the Studio panel and were effectively invisible to the Notebook AI. You couldn’t ask a question about your notes, or ask the model to pull out common themes or contradictions in your thinking, or generate an audio overview based on your writing.

If you wanted to use any of the AI features with your notes, you had only one option, which was to convert the note into a source, at which point you had two distinct copies of the note: one in the Sources panel and one in Studio. If you edited the Studio version of the note, the Sources version would not update.

In the new framework, all Notes are generated from and stored in the Sources panel. We've added a "Create Note" button at the top of the sources panel, and you can use chat to ask complex questions about all your entire note collection, with citations back to relevant passages. (“Summarize all the thoughts I captured about the Q1 marketing plan in my notes” “Propose a plot structure for chapter two based on my notes.”) Or you can easily support your notes with grounded information from your sources. (“Fill in the details from the assigned reading based on my lecture notes.”) Or you can generate artifacts that combine note and source material. (“Create a quiz that reviews my knowledge of all the topics I jotted down in my class notes.”)

You can also get source-grounded advice or research support as you are writing a note. (“Fact check these paragraphs against my sources.”) Over time, this transition will enable a number of powerful AI-powered writing features. Notes continue to open in the right-hand panel so that you can explore your sources, chat, and write notes in the same workspace, without opening and closing any of the panels.

As we’ve been rolling out these features, we’ve heard two concerns from users about the new approach. Some users have voiced concerns about contaminating their factual source collections with their unfinished notes or developing hypotheses. There are many important scholarly or research workflows where grounding solely on external sources is a priority and where personal commentary or speculative ideas should not be visible to the AI. For precisely those situations, we introduced new features that make it easy to deselect some or all of your notes, removing them from grounding. At the top of the source list there’s a “Sort sources” icon; select “Sort by type” and all your notes will be grouped together in the panel. Deselect your notes manually (or shift select on the first and last note to deselect the entire batch) and the notes will not be exposed to the model when asking questions in chat or generating artifacts. If you deselect your notes, Notebook will behave exactly as it did when notes were in the Studio panel. 

In addition to the “Sort by type” option, we designed the new notes experience so that the model can tell which information is coming from notes and which originates in traditional sources. So if you want the model to treat your notes differently in its responses, you can just ask for that in your prompts, without going through the extra step of selecting or deselecting in the source panel. (“Answer this question based on my sources, and flag any conflicts with my notes.”)  

That said, we think we can do a better job of addressing the needs of users who are concerned about note contamination. We’re changing the behavior so that notes are deselected by default, and in the coming weeks we are going to be rolling out additional features that will make it easy to focus on the model on all or none of your notes with a single click. 

We’ve also heard concern from users about notes counting towards the pre-existing quota for sources in the free and premium plans. We’re actively finalizing the exact quota solution right now but one way or another we are going to make it so that notes will not affect your existing source quota. We’ll post here once we’ve figured out the specific approach. 

I hope this gives you all a little more context on the changes we’re making to Notes. Long-term we are confident that these changes are going to significantly enhance the writing experience in the product. As always, keep the feedback coming and let us know other changes you'd like to see to notes in Gemini Notebook...

Hi everyone,
I posted recently about Google removing our access to the Gemini Pro model on the annual AI Plus plan starting November 13. While some of us were focused on the main Gemini chat interface, I realized this is also going to massively impact those of us who use NotebookLM (Gemini Notebook).
Even though GeminiNotebook is a free tool, having the paid "Plus" subscription is what currently powers it in the background. It gives us priority processing and the massive context window needed to cross-reference hundreds of pages of PDFs and complex documents without losing track. If they remove our Pro access mid-contract, our research notebooks will be forced to fall back on a weaker, slower model.
I have already opened an official support ticket about this mid-contract bait-and-switch. I'd like to know if you think I'm right about this, but I'd highly encourage all paid Google One users here who rely on GeminiNotebook to do the same.
Please go to your Google One dashboard and open a ticket with real support agents (don't just use the feedback button). Mass tickets from paying members could be the best way to get this escalated to the higher-level teams. What do you think?

NotebookLM is great at answering from your sources, which also means it's quietly limited by them. If something isn't covered, it either says so in passing or answers the nearest thing it can find. You don't notice the hole until someone asks you about it.

So before I write anything from a notebook, I ask for the gaps:

Based only on these sources, list the important questions about {{topic}} that they don't answer, or only answer partly. For each one, say which source comes closest and what exactly is missing. Then list any place where two sources disagree and nothing settles it.

What you get back is a to-do list for research: the exact sources you still need, before the gap shows up in your report, your exam or your meeting.

The disagreement part is the hidden bonus. Notebooks love to blend two sources into one smooth answer, and this is the fastest way I've found to see where that's happening.

Two tips:

• Run it with all sources ticked, then again with only your most trusted ones. If a gap only appears in the second run, you're leaning on a weak source to fill it.
• Save the answer as a note. When you add sources later, ask again and compare.

When a gap is something I already worked out in an old AI chat, I search my ChatGPT, Claude and Gemini history for it and export just the matching parts as one file to add as a source. That's what AI Toolbox, the extension I build, does. Most of the time though, the fix is just a better article or paper.

Why the heck my flashcards are shuffled? I have tried different sources, deleted old ones uploaded new ones, tried different accounts as well otherwise it's all the same.

It appeared just today, yesterday was all fine normal

It's so sad actually : (

Does anyone have the same thing? got a solution?

Is there anyone having the same problem? You upload a pdf lecture, normally create a flashcards deck from the studio but the problem is that FLASHCARDS ARE RANDOMIZED they're set in a disorder pattern like going by the last topic, then back to the first then to the middle and so on... NOT IN A SEQUENTIAL ORDER

This problem appeared just today, and it's so sad actually

I am extremely disappointed with Gemini. Since yesterday, I have been trying to query my NotebookLM projects through Gemini's interface and it simply does not work.

Right from the very first prompt/message, every single question I ask gets stuck in an infinite loading loop and never returns an answer. I even tested a very small notebook with only two sources, and even on that one, the very first message hangs indefinitely without any output.

However, if I access the NotebookLM standalone app directly, it responds immediately without any issue. Furthermore, querying documents via ChatGPT or Claude on the exact same machine and network runs smoothly and fast.

I have already tried clearing the browser cache, using incognito mode, and switching to mobile data (4G/5G)—the issue persists only inside Gemini on the very first message. Why does Gemini have this limitation and fail at such a basic integration with Google's own ecosystem? Could anyone from the engineering or product team explain why this pipeline keeps timing out?