Hey everyone,
I’ve been building a source-grounded research workspace called Gloss. I wanted the utility of Google’s NotebookLM, but without the black-box architecture, data privacy concerns, or forced reliance on proprietary APIs.
The goal here isn’t just a thin API wrapper; it’s a completely local, transparent RAG environment where you can actually audit the retrieval paths.
Under the Hood:
Built in Rust: Focused on speed, safety, and a low memory footprint. Custom Search Backend: It uses a custom semantic-memory crate I implemented with a hybrid search system (HNSW for dense vector search + TF-IDF/BM25 for exact keyword matching). Bring Your Own Models: fully supports local inference (Mistral, Llama 3, Qwen, etc.) via your local server setup, plus API integrations if you want them. Transparent RAG: No hidden prompts or shadow databases. It strictly adheres to the context constraints laid out in the workspace. You can see exactly what sources are being cited and why. Multi-Panel UI: Clean 3-panel split (Sources, Chat, Studio) for inspecting evidence alongside generation.
In the video, I demo the ingestion process and ask the system itself to compare its architecture against Google’s NotebookLM, it gives a pretty brutally honest breakdown of the trade-offs.
I’d love for you guys to check it out, tear it apart, and let me know what you think.
GitHub:https://github.com/RecursiveIntell/Gloss
💬 Discussion r/LocalLLaMA (49 points, 8 commentaires) 🔗 Source