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Burrow is a desktop research canvas that turns notes, documents, and AI responses into objects you can arrange and revisit on a board, rather than leaving them buried in a chat transcript. Its project documentation describes workspaces as folders on your disk and supports local or hosted AI services. Voice input takes extra setup: the installer does not include a speech engine, so you need to run a compatible transcription service or choose a hosted provider.
What Burrow is designed to do
Burrow targets open-ended research—work such as literature reviews, technical due diligence, and exploratory investigations—where useful material accumulates across notes, source documents, and conversations. The project frames the problem as research scattered among browser tabs, a notes page, PDF files, and memory. Its response is an infinite canvas where research material can be organized spatially and revisited.
The canvas supports notes, text, shapes, frames, tables, diagrams, documents, images, and freehand ink. The assistant can create and connect board blocks, read imported documents, search the web and literature, and invoke image-generation tools. Burrow says user and assistant edits share a command layer and undo history, so assistant changes are intended to remain part of the editable workspace. These are capabilities described by the project, not the results of independent testing. Burrow’s repository is the primary source for its feature and setup claims.
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How the workspace is stored
A workspace is a folder on the user’s disk, not just a conversation view. The repository’s example structure separates board data, transcript text, and imported or generated assets:
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workspace.jsonholds workspace metadata.board.jsonstores nodes, edges, ink, and viewport information.transcript.jsonlis an append-only text transcript.documents/andimages/hold document and image files.
The project says the transcript contains text only, not audio. This local-folder design makes the workspace’s organization and files visible on disk, but it should not be taken as an independent audit of security or as proof that every service interaction stays local.
What voice input requires
Burrow’s documented default voice path depends on a Whisper-compatible server that the user runs separately. The installer does not bundle a speech engine or model file, so voice recognition is not ready to use offline just by installing the app.
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Use a separately run speech service
The repository gives whisper.cpp, Speaches, LocalAI, and vLLM as examples of compatible services. You must set up and run a service, then configure Burrow to use it. The documentation also describes a microphone test for checking audio capture, speech detection, and transcription.
Choose a hosted transcription provider
Groq, OpenAI, and Deepgram are named as hosted alternatives. Selecting one avoids running the speech server yourself, but involves using a hosted service; the repository does not establish current provider pricing or terms. Burrow also describes an opt-in always-on listening mode, off by default: a local energy detector waits for speech, transcription begins after the wake phrase, and an indicator reports microphone state. Check the app’s microphone controls and test capture before relying on voice input.
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Model choices and data handling
The project describes support for Ollama and other OpenAI-compatible endpoints for local model operation. It also supports bringing provider credentials, which its documentation says are stored in the operating system keychain. Burrow characterizes configured provider calls as the data that leave the machine. Treat that as the project’s description of its design, not an independently verified privacy guarantee: the repository does not establish an external security audit or validate every provider’s handling of submitted content. Consider what you send to any configured service before importing sensitive research.
Which features the project says are verified
The repository reports that the assistant loop and block types have been checked against live providers, and that document import and paper search are working. It draws a different line for image generation: the feature is built, but the project says it has not been verified end to end with a live provider. The repository also says handwriting-reading vision has not been tested with a model that can actually see. An implemented interface or request format is not the same as a confirmed working end-to-end feature.
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Platforms and setup considerations
Burrow’s documented stack is Tauri v2, Rust, React 19, Vite, and TypeScript. The repository lists Windows, macOS, and Linux release options and notes that key storage and speech behavior vary by operating system. Since binaries and platform-specific setup instructions can change, consult the official repository for the current release and instructions before installing. The project article is titled “Burrow: a voice-driven research canvas that lives on your own disk”.
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Who may find Burrow useful
Burrow is most relevant if you want research materials and assistant output to become editable, spatially organized board elements, and you are comfortable configuring model or speech services. If you want voice transcription to work immediately without setup, the documented default is a poor fit: speech recognition requires a separately run server or a hosted option. Likewise, treat image generation and vision-based handwriting reading as less established than the project’s reported working document import and paper search.
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