According to XDA-Developers, writer Patrick built a personal research assistant using Obsidian and local AI to avoid the pitfalls of Google’s NotebookLM. He’s working on a novel with extensive source material and needed a tool that wouldn’t risk a sudden shutdown, like Google Stadia, or compromise his data privacy. The setup uses two key plugins: Smart Connections, which semantically links related notes with a relevance score, and Copilot for Obsidian, which allows for natural language queries of his entire vault using local models or his own API keys. This approach bypasses NotebookLM’s limits of 50 sources per notebook on the free plan and 300 on Pro, which he can exceed for a single chapter. His primary motivation is maintaining complete control and privacy, ensuring his research never leaves his machine unless he chooses.
The real cost of convenience
Look, NotebookLM is undeniably easy. You drag, drop, and chat. It just works. But here’s the thing: you’re trading that convenience for control. Your data sits on Google‘s servers, and while they promise privacy, it’s not a guarantee. And let’s be real—Google’s track record for maintaining projects long-term isn’t exactly stellar. For a short-term research sprint? Maybe it’s fine. But for a multi-year creative project like a novel? That’s a huge gamble. What happens if NotebookLM gets the Stadia treatment in two years? You’re left scrambling. The initial setup hassle with Obsidian starts to look a lot more like an investment.
Power tools beat all-in-ones
What’s fascinating about Patrick’s setup is that he’s not using one monolithic tool. He’s combining plugins to create a custom workflow. Smart Connections acts like an always-on, intelligent reference system, automatically showing him related notes. That’s huge for discovering forgotten connections in a massive vault. Then, Copilot for Obsidian becomes his conversational interface. It’s basically a private ChatGPT for all his notes. And because he can point it at a local AI model—think Ollama or LM Studio—his data never hits a third-party server. This modular approach is a classic case of a specialized, configurable system outperforming a slick, but locked-down, all-in-one solution. It’s the difference between buying a pre-built PC and building your own.
Privacy isn’t just a setting
The privacy argument here goes beyond just not wanting Google to see your stuff. For creators, there’s a very real concern about how their unpublished work could be used. AI companies are being sued for training on copyrighted material. Could your private notes in NotebookLM, even if not directly used, influence broader models in some way? The terms are murky. With a local Obsidian setup, the answer is simple: it can’t. The data stays put. This is crucial for professionals in fields like law, healthcare, or, yes, writing proprietary fiction. It’s about absolute ownership. When your work is your livelihood, you can’t afford ambiguity.
Is the hassle worth it?
So, should everyone jump ship from NotebookLM? Not necessarily. If you need to analyze a few PDFs for a college paper next week, NotebookLM is probably perfect. It’s fast and free. But if you’re building a long-term knowledge base—a research repository, a world-building bible for a novel, or a client project archive—the Obsidian path is compelling. The setup is more complicated, and you might need to pay for an API key or learn about local models. But once it’s done, it’s done. You own the system. You control the data. And in a world where our digital tools can vanish overnight, that’s a powerful feeling. You can get started by downloading Obsidian and exploring its plugin ecosystem. Just be prepared to tinker a bit. The payoff, it seems, is worth the effort.
