I saw many open-source versions of Notebook LM on GitHub, the most famous being lfnovo/open-notebook, but it requires Docker deployment. To be honest, I hate that everything requires Docker deployment. What I need is to click an icon and open it for use, rather than racking my brains to modify someone else's Docker Compose template to run a simple piece of software, while also consuming a large amount of my computer resources.
So I wrote a Notebook LM-inspired client based on Electron myself: KnowNote. I published it on X, and the response seemed pretty good. My repository's star count soared. This was my first open-source project, and it excited me immensely. I developed all the fancy features by following Notebook LM's functionality exactly—mind maps, flashcards, quizzes, and so on—hoping to truly bring Notebook LM locally.
But later everything began to change. Many comments under my X posts said: "Isn't this just reinventing the wheel?"; the GitHub star count stopped growing; a large number of boring and low-quality PRs flooded the entire repository. I was at a loss all of a sudden, became lazy, and the repository stopped being updated for nearly a year.
Recently, I calmed my mindset and truly began to look at the problem from a product manager's perspective. I went through many discussions on Reddit and X about Notebook LM and its derivatives, and found that everyone's mindset and the topics they focused on had also begun to shift: at first everyone was praising how excellent Notebook LM was, but now they were discussing "What do you actually use NotebookLM for?" "What's the difference between it and just throwing files to ChatGPT / Gemini?" "How can plugins, tools, and other products make it better to use?"
I realized that even Notebook LM cannot satisfy all users' needs. Merely copying it completely will only lead me down its old path. I increasingly feel that KnowNote should not chase after NotebookLM's feature list. I collected the questions everyone cared about most and found that most discussions revolved around this one core issue:
When you find an answer from a large amount of material through AI, how do you make that process more accurate, more transparent, and more comfortable?
It dawned on me that what I should truly solve well is this most core user problem, rather than drifting with the crowd.
In the past few days, I redesigned KnowNote's future roadmap, refocusing on AI Q&A, information retrieval, source citation, and the interactive experience built around these capabilities, using AI for reconstruction. Now I have released version v1.5.0, and I am very happy to reintroduce this project to everyone. Although many things may still not be easy to use, and I myself don't even have answers for many directions, I actually hope even more that more people will come in and give it a try right now. If you have used NotebookLM, Obsidian, various AI knowledge bases, or RAG tools, I especially want to know: What is the most frustrating thing for you when using these products or is there any need you have always wanted but existing products have never solved well?
If you are willing, you can try KnowNote, and you are also welcome to directly submit Issues, complain, or tell me that this direction simply doesn't hold up. Now I want to treat it more as a project that slowly seeks answers together with the community.