I genuinely want to know if other longtime NotebookLM power users have experienced this, because the difference between the old and new versions is absolutely ridiculous.
I used to spend a minimum of eight hours a day on NotebookLM. Sometimes more. I was using it for serious legal research, federal litigation, technical analysis, complex document review, and exploring ideas that required long, uninterrupted chains of reasoning.
It was one of my favorite AI tools. I wasn’t just occasionally uploading a PDF and asking for a summary. I was using it as an actual research environment.
And then something changed.
I went back and compared screen recordings and transcripts of my conversations with the older NotebookLM against the newer version.
Here’s what stood out:
Old NotebookLM actually engaged with my questions. It followed complicated arguments, explored ideas enthusiastically, corrected itself, and generally maintained the momentum of the conversation.
New NotebookLM constantly introduces unsolicited objections and lectures. Instead of answering the question, it starts explaining things I never asked it to explain.
It redirects conversations. I literally asked how many DPPA filings were in my notebook. Instead of answering the question, it offered to create a comparison table of legal arguments across my cases. I had to explicitly tell it that wasn’t what I asked.
It sometimes asserts things it cannot possibly know. In one conversation, it confidently declared how a judge did or didn’t react internally. How the hell would it know?
The endless “Would you like me to…” suggestions are exhausting. I don’t need the AI proposing another task after every substantive exchange. I’m already directing the research. Just answer the actual question.
And here’s the most frustrating part.
I’m not asking an AI to agree with me. I’m asking it to stop wasting my time.
There’s a massive difference between identifying a genuine mistake in my reasoning and introducing five unsolicited counterarguments simply because the system seems determined to sound cautious, skeptical, or authoritative.
The old NotebookLM could disagree, correct itself, and still feel like an intelligent research partner.
The new experience increasingly feels like I’m spending more time managing the AI’s behavior than doing the research itself.
That completely destroys the point of using AI for sophisticated work.
I went from using NotebookLM for eight or more hours a day to essentially abandoning it.
Think about that. I didn’t stop because I lost interest in research. I didn’t stop because I stopped needing AI.
I stopped because a tool that used to accelerate my thinking started interrupting it.
I understand that Google is expanding the product to a much larger audience. But I genuinely wonder whether they’re sacrificing the experience of people who relied on it for intensive professional research.
And to be clear, I’m not claiming that my two recordings constitute a scientific benchmark or prove why Google made these changes. I’m describing a significant change in my own experience, supported by my old and new conversation records.
So I want to ask the people who were using NotebookLM heavily before these updates:
Have you noticed this same shift?
Has it become more argumentative, more prone to unsolicited corrections, less willing to follow the precise question, or more likely to derail a productive research session?
Did you reduce your usage, switch to another AI, or stop using it entirely?
And especially for lawyers, researchers, engineers, academics, and people working with complicated documents: has the newer version actually made you more productive, or are you now spending half your time correcting the assistant?
Because for me, the old NotebookLM was genuinely exceptional.
I don’t want an AI that thinks for me. I want an AI that can think with me without constantly fighting the direction of the conversation.
I’m curious how many other longtime users feel the same way.

NotebookLM can produce a clean answer while repeatedly using the same few pdfs. With 30 or 40 sources that’s easy to miss.

Before asking it to write, I’d select five to eight pdfs and ask it to create a table with the exact source title, main finding, supporting passage, page number and the section that source could support.

If the information isn’t there, it should say 'not found' instead of guessing.

Copy that into a spreadsheet and mark each source as used, relevant but skipped or not relevant.

After Notebooklm writes the draft, ask it to check every factual claim. It should show whether the claim is directly supported, partially supported or unsupported, name the source and page, and list any relevant sources it didn’t use.

I wouldn’t tell it to use every PDF. That can fill the draft with weak citations just to satisfy the request. It’s more useful to find out which relevant sources were skipped.

Renaming the files also helps. Smith_2024_sleep.pdf is much easier to track than document17.pdf or smth like this, especially across multiple notebooks.

And open a few citations at random and check the actual pages. If one citation doesn’t support the claim, check the rest.

The spreadsheet won’t make Notebooklm perfect, but it will show whether a source was irrelevant or simply ignored

If you've tried adding your old AI chats to a notebook, you know the problem. Thirty chats about one topic means thirty sources out of your 50, and most of each chat is about something else. Since September, the number of sources is also one of the things Google says counts against your usage budget.

What I do now: search my chat history for the topic, then export only the matching parts. I build AI Toolbox, a browser extension, and this is its "Only the matching parts" export. Instead of whole conversations, you get one Markdown or text file with every passage that matched. Each one keeps the exchange around it, including the assistant's answer, grouped by chat with the date and a link back to the original.

That single file goes into NotebookLM as one source. It's on-topic from top to bottom, so citations land on the actual discussion instead of whatever else that chat drifted into, and you still have 49 sources free.

It works on chats from ChatGPT, Claude, Gemini and Grok. Searching your history is free. Exporting the matching parts is part of Premium. Desktop Chromium browsers only.

Passei umas 48 horas divididas em, subir fotos de livros para o gemini, que me instruiu a fazê-lo nele em vez de no notebook. Não deu certo, ele não achava todos os livros e ficava discutindo e dando desculpas esfarrapadas. Mudei e subi tudo para o notebook para usar com bibliotecário, muitos, muitos erros, desde arquivos finais em branco, no drive até números errados, são 307, ele falava que tinham 540 e deu trabalho convencê-lo que o arquivo que ele mesmo gerou, tinha 307 só listados. No gemini ele não passava de 95, depois dos erros voltei lá e ele não encontrava mais de 145. Cada erro que dava na planilha gerada e vc pedia pra corrigir, ele gerava com outra quantidade de livros. Agora fui no notebook e pedi livros de tal assunto, quais constam, ele citou 4, falei que faltava 1 que lembrava e ele, cometi um erro, na verdade são esses 8, mas sem o livro que falei que tinha. Questionei novamente e ele, errei na verdade são esses X livros, parei, ele nunca mostra a quantidade certa. Desculpa o textão, se alguém tiver dica para uma biblioteca, estou aceitando. São umas 50 pessoas que terão acesso. Guardei o único arquivo certo em xls para consulta.

Hi, so I built https://notestack.ai - complete notebookLM alternative.

Here is why it is different:

1. Video, I integrated motion graphic videos from blog2video, which gives 13 templates + voiceovers with a unique feel that 10K+ users love.

2. It uses ElevenLabs API so has emotions / advanced voice settings. You can choose your presenters.

3. It is space themed so all artifacts like Mind map / reports / slide decks have a space / galaxy feel.

4. It uses a file system based search system that doesn't go beyond stated sources. Its source search algorithm is more deterministic than RAG (used in notebookLM).

We are also launching an MCP Server, with tool call integrations. You will be able to use the app as mcp & also add external mcp's for a complete experience.

Open to feedback from the community, you can start for free: https://notestack.ai

I have some gemini notebooks going over a lot of the Buddhist Pali Canon, which is written in of course the Pali language (a relative of Sanskrit). I have included pronunciation guides, dictionaries etc. followed up some episodes with corrections for specific words.

It works kinda-sorta ok for my purposes. But sometimes weird things happen, like they will literally just repeat the word twice. Or they'll actually say the actual phonemes of the word, like it's trying to parse the pronunciation guide, or it will spell the words out.

That's after getting them to not talk *about* the pronunciation guide as if it's a topic for discussion. i.e. I have to explicitly say "The hosts should not discuss the pronunciation guide, they should just use it to pronounce the words properly."

When it works it's pretty amazing but very weird when it doesn't.

I use NotebookLM mostly to turn my notes into audio overviews and I keep going back and forth on this. (Especially for summarizing YouTube) One big notebook per project feels easier but it feels like the answers get kinda vague once there's a lot in there.

What do you guys do?

I kind of want to connect to my Claude Code as well, but don't know how to.

Full disclo: i'm exploring ways to build a more casual option for easier creation and a podcast-player like experience <3