Two different jobs, not two versions of one
NotebookLM is built to answer questions about documents. You upload a fixed set of sources into a notebook, and Gemini gives you cited summaries, explanations, and even Audio Overviews grounded in exactly those files. It is very good at interrogating a batch of material you already have.
Hjarni is built to be a memory. It is a knowledge base of plain Markdown notes that your AI assistants read and write over time, through an official MCP server. The point is not to summarize one upload, but to keep durable context that any assistant can use across conversations.
Read-only analysis versus a writable store
This is the cleanest distinction. NotebookLM reads the sources you give it and answers questions about them. It does not act as a free-form note store you keep adding to, and there is no official MCP or public API for outside tools to write into it. The community MCP servers that exist drive the web interface and are effectively read-only.
Hjarni's MCP server reads and writes. An assistant can search your notes, open one, update it, and create new notes, all against the same knowledge base. That is the difference between asking questions about a document set and maintaining a memory you can grow.
If you want cited answers over a fixed set of documents, NotebookLM is excellent. If you want a memory your AI can read and update, Hjarni is the better shape.
One model versus bring your own
NotebookLM is tied to Google's Gemini. That is fine if Gemini is the model you want, and the Audio Overviews feature is genuinely useful. But you cannot point Claude or ChatGPT at a NotebookLM notebook and have them work with it directly.
Hjarni does not bundle a model at all. You bring your own AI: Claude, ChatGPT, Codex, Cursor, or any MCP client. The same notes serve every assistant you connect, and the knowledge does not live inside one vendor's product.
When NotebookLM is the better fit
If your task is to understand a stack of PDFs, research papers, or transcripts, and you want cited answers plus an audio walkthrough, NotebookLM is a strong, purpose-built choice. It does that job well, and the free tier is generous.
When teams pick Hjarni instead
The case for Hjarni is not that NotebookLM is weak. It is that the two solve different problems. If your valuable context is something you keep adding to, and you want several AI tools to read and update it, a writable Markdown knowledge base with an official MCP server fits better than a read-only analysis layer.
Hjarni notes are plain Markdown you own, exportable as a ZIP anytime, hosted in the EU. That portability matters when the knowledge is meant to last.