This works pretty well. I tried it with this guidance prompt:

    You are both pelicans who work as data
    journalist at a pelican news service.
    Discuss this from the perspective of
    pelican data journalists, being sure
    to inject as many pelican related
    anecdotes as possible

Against this article: https://simonwillison.net/2024/Oct/17/video-scraping/

You can listen to the 7m40s resulting MP4 here: https://simonwillison.net/2024/Oct/17/notebooklm-pelicans/

Example snippets:

    You ever find yourself wading through
    mountains of data trying to pluck out
    the juicy bits? It's like hunting for
    a single shrimp in a whole kelp forest,
    am I right?

And:

    The future of data journalism is
    looking brighter than a school of
    silversides reflecting the morning sun.
    Until next time, keep those wings spread,
    those eyes sharp, and those minds open.
    There's a whole ocean of data out there
    just waiting to be explored.

NotebookLM is contributing to fake podcasts across the internet, with over 1,300 and counting:

https://github.com/ListenNotes/ai-generated-fake-podcasts/bl...

Google is taking a different approach this time, moving quickly. While NotebookLM is indeed a remarkable tool for personal productivity and learning, it also opens the door for spammers to mass-produce content that isn't meant for human consumption.

Amidst all the praise for this project, I’d like to offer a different perspective. I hope the NotebookLM team sees this and recognizes the seriousness of the spam issue, which will only grow if left unaddressed. If you know someone on the team, please bring this to their attention - Could you please provide a tool or some plain-English guidelines to help detect audio generated by NotebookLM? Is there a watermark or any other identifiable marker that can be used?

Just recently, a Hacker News post highlighted how nearly all Google image results for "baby peacock" are AI-generated: https://news.ycombinator.com/item?id=41767648

It won't be long before we see a similar trend with low-quality, AI-generated fake podcasts flooding the internet.

Nice, I've only scratched the surface of Notebook LM, mainly for dumping lots of component reference material (datasheets, reference guides, application notes, etc). The text querying works great, but the audio overview wasn't very useful when it stuck to the high level of the content. With some ability to steer the topic out might be quite useful!

Google Illuminate recently also introduced a customization feature. I use this customization with it:

audience=technical, duration=long, tone=professional & engaging

Not an improvement for me. I've been instructing NotebookLM for weeks now already by including the instructions into the sources. That way I have version control on my prompts and can easily drag into the sources upload. This requires finding my instructions and copying and pasting, there's also a 500 character limit which is very small, I have over 2000 characters for my standard prompts.

In a sea of similar tools, Google seems to have struck on something semi-viral with NotebookLM. Output can be mediocre, but with the bar for many podcasts being set at "read pages from Wikipedia", that's not bad at all for zero effort.

https://trends.google.com/trends/explore?geo=US&q=NotebookLM...

> With over 80,000 organizations already using NotebookLM

Really. "Using"? (as in an email from an org owned domain logged in to notebooklm page?..)

One day too late. ^-^

I realize now that this is actually a clever way to collect training data. If it were any company other than Google, I'd be like, Awesome toy. With them, I am uneasy.

Is there an open source tool that copies NotebookLM yet, or did anyone dig a bit into how the prompting is done to generate output in this dialogue format?

Ahh excellent! Podcast listings and Youtube weren't filling up with quite enough AI slop yet.