Open-source NotebookLM alternative for churned Google users

What to build

An open-source-based web app that replicates NotebookLM's core workflow: upload up to ~200 source documents, get grounded Q&A with citations, and export the research as mind maps, slide decks, and short explainer videos — aimed at students, researchers, and content creators who build research workflows around a tool like this.

Google has a track record of degrading or killing beloved products, and someone already shipped a public 'fix' for NotebookLM — a signal that a churn-ready user base exists for a source-grounded research tool that doesn't depend on Google's roadmap.

Demand

Students, researchers, and content creators who rely on NotebookLM for large-document Q&A and study-asset generation want a version they control, especially once they've been burned by Google changing or removing features they depended on.

• Reddit r/SideProjectAnecdote

  Post titled 'Google cooked NotebookLM, so I fixed it' framing Google as notorious for abandoning/degrading working products, prompting the author to build a replacement.

• Signal title/author claimOpinion

  Author states they built a fix/alternative in direct response to perceived Google mismanagement or feature degradation of NotebookLM.

• Opportunity brief (internal signal metadata)Analysis

  Describes the target feature set: source-grounded research/Q&A over up to 200 sources plus exportable mind maps, slide decks, and short videos, aimed at students, researchers, and content creators frustrated with Google's product changes.

Stack

• Ollama or vLLM for open-weight LLM inference (with OpenAI/Anthropic API fallback)
• Qdrant or Supabase pgvector for retrieval
• LlamaIndex or LangChain for RAG orchestration
• Unstructured.io or PyMuPDF for multi-format document ingestion
• Remotion + TTS API (ElevenLabs/OpenAI TTS) for slide/video export
• Vercel + Supabase for hosting and auth

Solo + AI difficulty

Source-grounded Q&A over many documents with citations is straightforward with existing RAG tooling and shippable in a few weeks; the hard, slow part is building a reliable mind-map/slide/video export pipeline that matches NotebookLM's output quality. A text-Q&A-plus-mind-map MVP is realistic in 3-4 weeks solo with AI coding help; full feature parity including video generation is more like 2-3 months.

Entry threshold

Core RAG-over-documents stack (embeddings, vector search, LLM Q&A) is buildable solo in weeks using existing APIs; the harder, higher-effort layer is the video/voice generation pipeline (ElevenLabs integration, motion graphics rendering), which this poster reused from a prior 10K-user product rather than building from scratch.

Window

6-9 months

Where to find first users

• r/SideProject (where the original signal was posted)
• Product Hunt launch
• r/LocalLLaMA and open-source AI communities
• Show HN on Hacker News

Competitors

• Open Notebook
• SurfSense

Counter-signals & risks

• NotebookLM benefits from deep integration with Google's Gemini models, Drive, and Workspace ecosystem, which an open-source alternative may struggle to replicate at comparable quality or cost.

• User frustration expressed in a single blog/signal may not represent broad market churn; Google could reverse or improve the criticized changes before an alternative gains traction.

• Building and maintaining a multi-format export pipeline (mind maps, slide decks, video generation) alongside large-context document Q&A is resource-intensive and may be difficult to sustain as a lean or open-source project against a well-funded incumbent.