Like many developers and researchers, I've spent the past two years experimenting with local LLMs (via Ollama) and local RAG. But existing desktop frontends repeatedly hit two frustrating walls:

The 1D Chat Trap : Linear chat streams work well for simple Q&A, but break down during deep research synthesis. Earlier reasoning turns scroll off the top, and you cannot visually compare multiple document excerpts side-by-side.

The Electron Memory Tax : Most desktop AI wrappers idle at 700MB–1GB RAM before you even load model weights into VRAM.

To solve this, we built Knovum — an open-source, local-first desktop AI knowledge workspace built on an infinite 2D canvas with Tauri 2, Rust, and Vue 3.

Today we just shipped v0.0.2 , so I wanted to share the engineering decisions behind the architecture, what we learned building it, and what's new.

Core Architecture: 45MB Idle RAM & Local-First Storage

┌────────────────────────────────────────────────────────┐ │ Vue 3 Spatial Frontend │ │ (Konva Infinite 2D Canvas +…