Reor is an AI-powered desktop note-taking app: it automatically links related notes, answers questions on your notes, provides semantic search and can generate AI flashcards. Everything is stored locally and you can edit your notes with an Obsidian-like markdown editor. The hypothesis of the project is that AI tools for thought should run models locally by default. Reor stands on the shoulders of the giants Ollama, Transformers.js & LanceDB to enable both LLMs and embedding models to run locally: 1. Every note you write is chunked and embedded into an internal vector database. 2. Related notes are connected automatically via vector similarity. 3. LLM-powered Q&A does RAG on your corpus of notes. 4. Everything can be searched semantically. One way to think about Reor is as a RAG app with two generators: the LLM and the human. In Q&A mode, the LLM is fed retrieved context from the corpus to help answer a query. Similarly, in editor mode, the human can toggle the sidebar to reveal related notes "retrieved" from the corpus. This is quite a powerful way of "augmenting" your thoughts by cross-referencing ideas in a current note against related ideas from your corpus.