I Found the Self-Hosted NotebookLM Devs Actually Want (Open-Notebook)
BBetter Stack
컴퓨터/소프트웨어창업/스타트업AI/미래기술
Transcript
00:00:00Notebook LM, it's amazing.
00:00:02You upload a paper, a document, maybe even a code base,
00:00:05and now you can chat with it and summarize it.
00:00:08But then there's the part nobody likes talking about.
00:00:10You still have to upload all that stuff to Google.
00:00:14This is Open Notebook.
00:00:15It has over 27,000 stars on GitHub.
00:00:18It's self-hosted, and it asks a very simple question.
00:00:22What if you could get the Notebook LM experience,
00:00:24but with dev-level control?
00:00:26Today, I'm gonna spin it up, test the workflow,
00:00:29compare it to Notebook LM and anything LLM,
00:00:32and answer the real question.
00:00:34Is this actually useful for devs,
00:00:36or is it just another AI wrapper?
00:00:43Open Notebook is a privacy-first,
00:00:45self-hosted alternative to Notebook LM,
00:00:47but that undersells it a little bit,
00:00:49because this is not just Notebook LM, but open source.
00:00:53It gives you a Notebook LM-style research workspace,
00:00:57multi-model support, podcast generation, local first options,
00:01:01and a REST API you can actually build on top of.
00:01:04And that's the part a lot of people are actually caring about.
00:01:07Most people look at this and they think one thing.
00:01:09Cool, I can make an AI podcast from PDFs.
00:01:12Bravo.
00:01:14Devs look at it and we think something slightly different.
00:01:16Can I plug this into my workflow?
00:01:19That's one real question.
00:01:20Can I use local models with it?
00:01:22Can I automate research summaries?
00:01:25And answering all those questions
00:01:26is where Open Notebook gets interesting.
00:01:28You're not locked into Gemini.
00:01:30You can use different providers,
00:01:32including local models through Alama.
00:01:34You can self-host it.
00:01:35You can customize the podcast experience
00:01:37with different speaker profiles.
00:01:39And because there's an API,
00:01:41this can become part of your stack.
00:01:43Not just another tab in your browser.
00:01:45If you enjoy coding tools that speed up your workflow,
00:01:48be sure to subscribe.
00:01:49We have videos coming out all the time.
00:01:51All right, let's actually run this
00:01:52so you can see it in action.
00:01:55Open Notebook is Docker first.
00:01:57That's awesome.
00:01:58So if you're already comfortable with containers,
00:02:00this is really familiar territory.
00:02:02Run the Compost setup,
00:02:04wait for the services to spin up,
00:02:06and then open the app in your browser.
00:02:08Now that it's going, we can create a new notebook.
00:02:10Think of a notebook like a project-specific research workspace.
00:02:13Instead of dumping everything into one giant AI chat,
00:02:17you can separate things out.
00:02:19One notebook for code bases,
00:02:21one for project research,
00:02:22one for academic papers,
00:02:24internal docs,
00:02:25all that stuff.
00:02:26Now we can add in our sources.
00:02:29This could be things like a PDF,
00:02:31a readme,
00:02:31documentation,
00:02:32a research paper,
00:02:33or really anything you want the system
00:02:35to actually reason over.
00:02:37And the important word there is source,
00:02:39because the goal is not just generic AI chat.
00:02:43The goal is grounded answers based on the material that you give it.
00:02:48So let's ask now a more technical question here.
00:02:51Maybe I can ask something along the lines of,
00:02:53what are the main components of this project,
00:02:55and what would I need to change if I want to extend it?
00:02:58It's doing its thing.
00:03:00This is the basic notebook LM style experience.
00:03:03We add sources,
00:03:04you ask questions,
00:03:04you get answers grounded on those sources.
00:03:07But here's the main thing.
00:03:08This is where it stops feeling like a clone of something
00:03:11and starts feeling more dev-friendly,
00:03:13more of something maybe on its own.
00:03:15You can choose which model provider you want to use,
00:03:18and we're not locked into that vendor like Gemini,
00:03:20like I said.
00:03:21Hosted models,
00:03:22absolutely fine.
00:03:24Local models,
00:03:25also absolutely fine.
00:03:27You get to pick the trade-off
00:03:28between quality,
00:03:29speed,
00:03:30cost,
00:03:30and privacy.
00:03:31And then there's the feature
00:03:32most of us will click first,
00:03:34the podcast generator.
00:03:36Notebook LM made AI podcasts
00:03:38feel actually pretty cool.
00:03:40If you haven't played with it,
00:03:41maybe you should.
00:03:42If I run it here,
00:03:44well,
00:03:44something else happens.
00:03:46Take a listen.
00:03:46It's a game changer for researchers
00:03:48looking for autonomy and privacy.
00:03:50Absolutely, Alex.
00:03:52I think one of the coolest aspects of Olama.
00:03:54Cool, right?
00:03:55But Open Notebook gives you more control
00:03:57over that format.
00:03:58You can generate podcasts
00:04:00from your sources,
00:04:01configure the structure,
00:04:02and use multiple speaker profiles
00:04:04instead of being stuck
00:04:05with one fixed style.
00:04:07So instead of getting
00:04:08generic AI hosts
00:04:10explain a PDF,
00:04:11you can create something
00:04:12more specific.
00:04:14You could say something like
00:04:15a product manager
00:04:16and a backend developer
00:04:17debating an architecture doc.
00:04:19And that sounds small
00:04:20until you use it on something
00:04:22that's honestly painful.
00:04:24A long RFC,
00:04:25a dense white paper,
00:04:26a boring API spec,
00:04:28all that stuff.
00:04:29It's a way to make dry information
00:04:30just easier to consume.
00:04:32Now, let's compare it
00:04:33to the tools we already know
00:04:35that are out there.
00:04:36Let's start with Google Notebook LM.
00:04:39Notebook LM is great.
00:04:40It's easy.
00:04:41It works really well.
00:04:41And for a lot of us,
00:04:43honestly, that's probably enough.
00:04:45But the trade-off behind all that
00:04:46is the control.
00:04:48Open Notebook gives you
00:04:49self-hosting,
00:04:50multi-model support,
00:04:51local model options,
00:04:52customizable podcasts,
00:04:54and API access.
00:04:55So if you're working
00:04:56with sensitive docs,
00:04:58private research,
00:04:59or internal material,
00:05:01Open Notebook has
00:05:01the stronger privacy story here.
00:05:04Now, here's the catch to all that.
00:05:06Is Open Notebook as smooth
00:05:07as Notebook LM?
00:05:08No, not always.
00:05:10Notebook LM has the advantage
00:05:12of being polish-hosted product.
00:05:15It's from Google, right?
00:05:16Open Notebook is more flexible,
00:05:17but it still feels like
00:05:18a dev-oriented open-source project.
00:05:20That's not a deal-breaker.
00:05:22It just means you should know
00:05:23what you're actually choosing.
00:05:24Now, compare it to anything LLM.
00:05:27Honestly, that is a really cool tool.
00:05:30Anything LLM is also popular
00:05:31in the self-hosted AI space,
00:05:33but it wins in a different way.
00:05:35It's easier to get started with.
00:05:37It has a desktop app.
00:05:38It has no code agent workflows.
00:05:40That's great.
00:05:41For a non-technical user,
00:05:42that may be the first step.
00:05:44But Open Notebook feels more focused
00:05:46on the Notebook LM-style
00:05:48research experience.
00:05:50Now, let's be honest
00:05:50about what people like
00:05:51and what people are actually
00:05:52complaining about.
00:05:53The huge win here
00:05:54is going to be privacy.
00:05:56If your work involves sensitive docs,
00:05:58private code, client research,
00:06:00or anything you would be hesitant
00:06:02to upload to a hosted AI tool,
00:06:04then self-hosting really matters here.
00:06:06That is the main reason
00:06:07that Open Notebook
00:06:08actually exists in the first place.
00:06:10Then you have model flexibility.
00:06:12You're not forced into one provider.
00:06:14Yes, I can choose the ones I want.
00:06:16Huge win.
00:06:17That means we can choose
00:06:19what we need
00:06:19based on what we're working with,
00:06:21but it also creates a new problem.
00:06:24You have to make a choice.
00:06:25We also have the podcast customization.
00:06:28Tried on a huge project spec,
00:06:29a dense API, Docker,
00:06:31a long research paper,
00:06:32and it actually starts to make sense.
00:06:33Finally, the API is a big deal
00:06:35for a lot of us.
00:06:36You can imagine workflows
00:06:38like creating research briefings
00:06:40from GitHub issues
00:06:41or sending outputs
00:06:43into Slack, Linear, or Notion.
00:06:45Great.
00:06:46Now, the bad thing about all this,
00:06:48or maybe things
00:06:49that we don't quite like first,
00:06:50setup is Docker first.
00:06:52For most of us, honestly, that's fine.
00:06:54For everyone else,
00:06:55maybe that's a wall.
00:06:56This is not yet the download one app
00:06:59and everything just works.
00:07:00Second, it's still a newer project,
00:07:03so some things are still catching up.
00:07:05And then quality depends
00:07:06on your models and your setup.
00:07:08So the honest take here
00:07:10is kind of simple.
00:07:11Open Notebook is not perfect.
00:07:12Then again, no tool is perfect.
00:07:14That's why we have
00:07:14all these different tools.
00:07:16But the direction it's going in
00:07:17is very good.
00:07:18It's not for everyone,
00:07:19but give it a try
00:07:20if you want self-hosted
00:07:21research backend,
00:07:23if you have docs
00:07:23you don't want to just upload to Google,
00:07:25or if you want to build
00:07:27custom workflows on top of the API.
00:07:30The stack includes
00:07:31modern front-end,
00:07:32a Python backend,
00:07:33SurrealDB,
00:07:34and an AI abstraction layer
00:07:35designed to work across providers.
00:07:37So it can feel like something
00:07:39you can actually extend,
00:07:41not just something that we use.
00:07:42If you enjoy coding tools like this,
00:07:44be sure to subscribe
00:07:45to the Better Stack channel.
00:07:46We'll see you in another video.
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