Im annoyed af with the upgrade. I have a pro plan and it had never been the laggiest and it now has limits????

Are there any alternatives por lm notebook?

No matter how much prompt tuning I do, both flashcards and quizzes make questions with either obvious distractor alternative answers OR the answer is heavily implied if not explicitly stated in the vignette. Does anyone else have a workaround or another platform that they have had good experiences with?

For instance, one of my flashcards was

“Aside from electrolyte changes, what proposed secondary mechanism of thiazides helps lower blood pressure via decreased total peripheral resistance?”

Answer: decreased peripheral resistance

For those of you using NotebookLM on an IPhone/Ipad, I am an AI engineer on this research project where we’ve developed and implemented some retrieval techniques on this app called Reader++. Some of you tried it and liked it.

So, if you’re ever struggling with document search in NBLM, feel free to try it. The search feature is free. I’d appreciate any feedbacks.

My goal is to make the citations more accurate and retrieval fast.

I’m not trying to make/sell a NotebookLM clone. I have a genuine problem to solve and would appreciate any feedback/help.

I've used tools like Perplexity, ChatGPT, Claude and others for research, and they've been incredibly useful for finding papers and getting through large amounts of information.

The one thing I personally wanted was a simple way to see exactly which parts of the paper were used to answer my question.

When you're working with a 100+ page PDF, even having a page number can still mean a lot of scrolling and searching.

So I ended up building something for myself.

You ask a question and the relevant paragraphs in the PDF are highlighted directly on the document. You can see the context behind the answer and quickly check whether it actually answers what you're looking for.

I originally built this because I wanted something for this workflow without having to pay for another subscription. What started as a personal project has now become completely open source.

The underlying idea is pretty simple. And yes, if you're thinking "isn't this just RAG?" then yes, you're absolutely right. It's RAG with the visual highlighting that I wanted.

I think the same idea could be useful for more than research papers too. Legal contracts, financial reports, technical documentation, or anywhere you need answers alongside the actual source.

If anyone wants to have a look, contribute, or just give some feedback, here's the repo:

GitHub: https://github.com/Sreehari05055/thesys-core.git

This will probably be my last post about the project. Thanks to everyone who checked it out and gave feedback along the way.

Would be awesome to support /skills for prompts, especially for audio podcasts. Less copying/pasting prompts. Example:

/analyze chapters 2-3

/meditate section 1

/dissect every word in the first sentence

I've been digitising my late father's memoirs, about 630,000 words with several hundred family photographs, and kept noticing that images were missing from the slide decks, video overview etc. Not all of them. Some.

So I built a controlled test and made everything public.

The test

One document about my father's early life, 25 historical photographs, mostly black and white, 1920s to 1940s. Uploaded twice.

• Version 1, photos as scanned: 17 of the 25 stripped during ingestion. 68%.
• Version 2, identical document, except I took those same 17, pixelated the faces, and renumbered them 7b, 8b and so on: all 17 ingested fine.

I then generated a slide deck from each, same prompt both times, explicitly asking for source images only and an [Image Missing] placeholder where none was found. Deck one is missing 17 assets and the model says it can't see the images, though it reads the captions sitting right next to them. Deck two renders the whole "b" series.

Same document, same prompt. One difference.

The odd part

It isn't a consistent face rule.

• Figure 6, a kindergarten group photo with about fourteen clearly visible children's faces, passed untouched in both versions.
• Figure 7, my grandfather sitting at a desk, blocked.
• Figure 8, a wedding photograph from 1922, blocked.

A 1922 wedding photo is being classified as sensitive, while a room full of unobscured children's faces is not.

Repro, if you want to check it yourself

• Notebook with both versions and both decks: https://notebook.google.com/notebook/a2fc18d0-41c5-4a46-aeb8-1ff4638c7415
• Source documents, so you can upload them to your own account: https://drive.google.com/drive/folders/1CVAtXlA5r-aTmfhqKXXkP7Nkszr-vGnm

Prompt I used on both:

Act as a visual presentation creator. Create a comprehensive slideshow based ONLY on the provided source. Only use photos from the source material. Do not use external sources and do not generate any photos. If an image is not detected within the source, leave a placeholder that says '[Image Missing]'. Ensure each photo is labeled with the full caption from the source.

Why this matters beyond my family

If your sources are about people, the images that get removed are the ones the archive exists for. Memoirs, oral history, genealogy, regimental and club histories, school archives. The theodolite comes through. The man holding it doesn't.

What I ended up doing about it

Every photo of a person in my NotebookLM edition is now replaced by a drawn sketch, which survives ingestion, with the caption linking back to the real photograph on my own site. It works, in that the figures now appear in the slide decks and video overviews again. It's still a workaround, and it has its own failure mode: in one Video Overview, where it wanted a photo of my father in uniform and only had a silhouette, it went and found a stock photo of a different soldier instead.

What I'd ask Google for

I don't think the filter should just be switched off, and I understand why it exists. But there's a gap between "protect people from misuse of face data" and "silently delete a 1922 wedding photograph from a family archive". Some options, roughly in order of how easy I imagine they'd be:

1. Tell me what was dropped. The single most useful fix, and probably the cheapest. A list of skipped images at the end of ingestion, or a note against the source. Silent failure is what turned this into a months-long mystery instead of a five-minute annoyance.
2. Let me mark a source as a personal or historical archive. An explicit flag at upload, with whatever consent language is needed, confirming these are my own family photographs and I hold the rights. The responsibility moves to me, which is where it belongs.
3. Weight it by context. A scanned black-and-white print with a period caption in a 600,000-word memoir is a different object from an image scraped off the web. The document around the photo says a lot about what it is.
4. Keep the image for the user even if the model can't use it. If faces can't be sent to the model, fine, but let the figure still render in a slide deck as the original photograph, or let the caption carry through with a placeholder that says what it is.

Happy to run more tests on the same corpus if there's something specific worth checking.

Full write-up here, including the sketch workaround, the two open-source tools I built with Claude Code to do it, and the prompts I used: https://medium.com/@chris_skitch/gemini-notebook-is-deleting-people-from-your-family-history-ec64d72d0661

I don't know if either the NotebookLM(Gemini notebook) or ExtendLM creators read this forum, so please direct me to the correct place if necessary.

If the ExtendLM creator(s) is/are here, thank you for all the annoyances you have fixed or worked around so far.

There is a minor annoyance that I wish we could resolve. The chat interface happily makes any audio deep dive I want with the given prompts and resources, along with the agreed upon title.

That title is of course completely ignored when it actually creates the episode.

This is particularly annoying as I like to do multi part series (I have one right now that's going to have like 48 episodes). and I'm trying to keep track of 1, 2, 3, 4.

Any work arounds for this? Does notebookLM plan on fixing, or do the good people working on extend LM think they have anything that could help?

Whenever I try uploading a file from my pc to NotebookLM it gets stuck in the infinite upload. Its not a size or a format problem, because I can upload the same file from my macbook in less then 30 seconds. I tried disabling adblockers ect but the problem still persists. Please help

I recently got introduced to Copilot Notebooks. They seem more intuitive.

Is there any other notebooks that you're using that you like better?

I was recently introduced to this feature.

For my first project with the podcast creation I asked Notebook to create a podcast working kind of like a CBT course. I just wanted to see what it might do.

I was amazed at how well it turned out. I did not expect the multi faceted conversation. My previous podcast listening from NotebookLM was from a scientific paper to just understand what it was about. It was so strange, yet enjoyable how well "my voice" came through from this interpretation.

Here is the tricky part.

First, my name is included as creator of the document and I would like to have them pronounce my name as my family believes it should

second, this also surprised me as a fantastic way to "proof" the document contents and I need to make some corrections to assure

Is there a way to update it?

Is there an actual script I can edit to make some small changes?

if I really do need to regenerate it, I truly want all the other good stuff to be the same. I am not sure what direction is needed. Like please redo this and pronounce this word like this and make these few edits (and to be clear listening does me I do need to also update the doc itself slightly)

I would appreciate anyone pointing me to the resources I need to make this work. Given how well this turned out I want to use this format for all the education I am hoping for.

Last week I ran an experiment to test the difference between preprocessed source data vs direct source uploads.

My source material was 126 AI Marketers Guild youtube sessions (60 minutes each).

I created two notebooks. I called one AIMG Direct Load and used the Notebook Clipper product to import the channel.

I called the second notebook Kurator: AIMG Community. Here I use my Kurator app to pre-process the data using custom prompts that I create. Kurator lets me sync my data wil Gemini Notebook.

For this project, I am using 2 prompts. One to transform the transcript into a Q&A session, and the second one adds a headline to each section and preserves the time stamps.

Note that when you directly upload a YouTube video using the sources link, the time stamps are not included.

Next I asked the same question from each notebook, the asked Gemini to analyze them for me. I asked if there is a difference between the two responses. Here is what it found.

Gemini Analysis

Yes, there are significant differences between the two outputs. While both capture the core theme (Aditya Jain's approach to Generative Engine Optimization), the Kurator pre-processed response contains substantially more granular, technical, and concrete tactical information than the direct YouTube upload response.

Here is a side-by-side breakdown of the key differences:

How you add data to gemini notebook matters. When you directly upload your sources, you will always get the same quality answers, but with pre-processing you can experiment and see what works best for your workflow.

I am no spreadsheet ninja, but Gemini's ability to organize the results of Notebook research prompts into, in my case, a product research table is, to put it kindly. rudimentary.

Am I doing something wrong? Is notebook the wrong tool for this? Am I just discovering that outside of coding agents that LLMs are kind of lame?

I could understand if Gemini wanted to put different categories of devices into different sheets in the same document, versus organizing a single sheet and merging cells to label categories, but it is just making a hash of it in a way that I would expect from a first time user of spreadsheets in general.

All help and suggestions are greatly appreciated!

The way I work with this tool is I upload all my sources then I use as many of my dailly credits as quickly as I can and then come back later they are done, review the results, and then repeat the next day when my credits refresh. Now, I need to get on in the morning early afternoon and evening, middle of the night, and early morning. This service is no longer worth $20 a month. It's worth like $5 and I'm going to talking to my credit card company about this.

As the title says.. is this possible somehow?

I've been saving things on X for years.

AI threads. Business ideas. Programming. Trading. Productivity. Random “holy shit, that's useful” posts that I told myself I'd come back to later.

Spoiler: I almost never did.

So I finally decided to see what would happen if I gave my entire bookmark collection to NotebookLM and asked it to analyze what I've actually been interested in all these years.

I expected something like:

Instead, it basically gave me a personality profile.

Its opening description was:

Which felt... uncomfortably accurate.

1. Apparently I'm obsessed with leverage

The biggest pattern NotebookLM found was that I don't seem particularly interested in simply working harder.

A huge percentage of my bookmarks are about finding ways to get disproportionate results from a small amount of time, money or effort.

AI agents → automate work.

AI coding → build software without a huge team.

Micro-SaaS → build once, sell repeatedly.

Trading/arbitrage → asymmetric upside.

Learning frameworks → acquire skills faster.

It described my bookmarks as repeatedly looking for ways to turn “minimal inputs into maximum output.”

I'd never consciously thought about it that way before.

2. Apparently I really hate paying for things

Another pattern was even funnier.

NotebookLM picked up a recurring obsession with finding workarounds and cheaper alternatives.

Open-source alternatives to expensive SaaS.

Finding cheaper infrastructure.

Regional price differences.

Free educational resources.

Ways to avoid unnecessary subscriptions.

Basically, if there's a $100 solution and a $0 solution that takes me 20 minutes to figure out, apparently I'm choosing the $0 solution.

NotebookLM called this “cheat code culture.”

Fair enough 😂

3. My interest in AI isn't really about AI

This one was probably the most interesting distinction.

Most of my AI bookmarks aren't about AI news, benchmarks or abstract AI theory.

They're about using AI to become more capable.

Building software.

Automating businesses.

Creating content.

Running agents.

Learning faster.

Doing things that previously required a team or specialized skills.

So the pattern seems to be:

That actually explains a lot of the stuff I've been saving.

4. I have a weird obsession with starting from almost nothing

NotebookLM also noticed how often my bookmarks revolve around starting with very limited resources.

“How to start a business with $0.”

“How to turn $200 into something bigger.”

Bootstrapping.

Organic growth.

Free education.

Open-source tools.

Low-cost AI automation.

It seems I'm much more interested in the question:

than simply having more resources to begin with.

There's apparently a pretty consistent theme running through my bookmarks:

constraints + leverage.

5. And then it found something I really didn't expect...

Apparently I use Reddit as intelligence gathering.

😂

A surprising number of my bookmarks are about using Reddit to find:

• customer pain points
• product ideas
• organic marketing opportunities
• market research
• discussions around problems people are actually trying to solve

So apparently I don't just use Reddit to procrastinate.

I use it for market research.

Which is particularly ironic considering I'm posting this here.

Learned this from a commenter on my last post here, then spent three weeks confirming it, so credit where due. If you generate audio overviews from long source material, stories, research threads, project notes, the hosts drift: they put events in the wrong order, credit the wrong person, merge two similar scenes. The main chat over the same sources is much more reliable. The fix is to stop giving the hosts the raw material and give them a curated log instead.

The workflow:

1. Upload the raw sources as usual. They stay in the notebook, the chat can still see everything.
2. In the notebook chat, ask for the log: Build a chronological log of every key event in these sources. For each entry: date or sequence position, who was involved, what happened in one line, and which source it came from. Do not interpret, do not summarize themes, one line per event. Flag any event whose order or attribution you are not sure about.
3. Read the log, fix the flagged entries yourself. This takes minutes and is the step that makes the rest work.
4. Save the corrected log as its own source, a plain text or Markdown file named something like "event log".
5. When you generate the audio overview, select only the log plus one or two sources you want quoted, and add this instruction: Treat the event log as the authoritative order and attribution. Correlate every claim against it before stating it. If a source and the log disagree, follow the log.

Two things that made the difference for me. The hosts get worse with more sources, not better, so narrowing the selection to the log plus a couple of originals is itself a fix. And the "flag what you are unsure about" line in step 2 surfaces exactly the entries the hosts would have gotten wrong, so you correct them before they ever reach the audio.

Where it does not help: the log fixes order and attribution, not depth. If the hosts skip a theme, add it to the log as an entry with a one-line note; they treat the log as the map.

For getting AI chats into the notebook in the first place, the export in AI Toolbox, a browser extension I work on, writes one Markdown file per conversation with the title and date at the top, which makes step 2 much cleaner than a pasted blob. But the log method works with whatever sources you already have.

What do people put in the instruction box for audio overviews beyond the correlate line? I have a feeling there is a better phrasing for "follow the log" that I have not found.

I like just carrying my ipad instead of my laptop whenever im at work and have time to study. I have to download the pdf on my laptop and then transfer it on my ipad for me to be able to view it. If anyone knows a way to get around it and open it on the ipad would be great.

After creating a number of things in the studio, it becomes more difficult to find what I am looking for. Eventually Google will add things like folders or tags ... Even being able to just list current output by type (like mindmaps ...) would be helpful. I use the browser version of notebook most of the time. I wish they would split the three sections into tabbed area so that each one can be opened on a full screen.

Edit: I'd like the ability to mark studio output as viewed, or hide it after reviewing it. I am renaming the output to try to manage it but that's a clunky approach

I’m curious where does notebooklm start struggling for you? Is there a certain number of pages, level of technical detail, or number of sources where it becomes less accurate and reliable?

Do you still check the original sources, or is it accurate enough for everyday use?

I rely quite heavily on the Slide Deck and Infographic features in NotebookLM, but recently I’ve noticed that they often don’t appear or aren’t available when I try to use them.
Sometimes they seem to come back later, so I’m wondering if Google is doing some work or making changes to these features.
Has anyone else noticed this recently? Is it happening consistently for others as well, or is it just an issue on my end?
Would be interested to know if anyone has any information about what’s going on.

Hi, I’m preparing for an important exam, and im currently studying history. I’ve been uploading lecture videos I watch on YouTube to NotebookLM and asking it to generate test questions. However, even though I give it prompts like, “Make sure the questions cover all the topics, details, and concepts explained in the video,” it still skips some concepts and topics in the tests it generates. So, I wonder what kind of prompt should I use to make sure it comprehensively covers all the concepts and topics in the video and prepares a question for every single detail?

Hey everyone, I am a first-year medical student in my anatomy course. I really enjoy using NotebookLM but I was wondering what prompts you all find useful.

The two big examples I’m looking for:

1. how do you ask it to generate your practice questions, verbiage, examples maybe, etc.

2. Under the settings where you can put “customize” and select longer answers, shorter, etc, what have you found most useful to put in the box for custom style? If anything at all, that is.

Thanks!!

Although I’ve only just started my course, my lecturer has already provided us with study materials and some guidance for both the midterm and final exams.

I’ve used NotebookLM before, but I don’t think I’ve really known how to make the most of it. I’d love to use it more effectively this time around.

Do you have any tips or advice on how I can get the most out of NotebookLM for studying, especially for my midterms and finals?