| --- |
| title: Knowme |
| emoji: 🍄 |
| colorFrom: red |
| colorTo: indigo |
| sdk: gradio |
| sdk_version: 6.16.0 |
| python_version: '3.13' |
| app_file: app.py |
| pinned: true |
| license: mit |
| short_description: Stop losing ideas. Capture, connect, recall. |
| tags: |
| - build-small-hackathon |
| - backyard-ai |
| - tiny-titan |
| - nvidia-nemotron |
| - off-brand |
| - off-the-grid |
| - field-notes |
| - track:backyard |
| - sponsor:nvidia |
| - achievement:offgrid |
| - achievement:offbrand |
| - achievement:fieldnotes |
| --- |
| |
| # Mycelium — Personal Knowledge Agent |
|
|
| > Capture fast. Think later. Let the system surface what matters. |
|
|
| Mycelium is a local-first AI knowledge companion that closes the loop between saving something and actually learning from it. No more screenshot graveyards or forgotten browser tabs. |
|
|
|
|
| ## The problem |
|
|
| Everyone has the same graveyard: saved links, screenshots, notes-to-self — all gone dark in a week. The capture habit exists. The recall loop doesn't. |
|
|
| Mycelium fixes the loop. |
|
|
| ## What it does |
|
|
| - **Capture** notes, URLs, and images — each processed into a structured summary with intent classification (`learn` / `act` / `reference` / `ephemeral`) and semantic tags |
| - **ASK** — semantic search across your knowledge base with LLM synthesis, follow-up questions, and Feynman self-testing |
| - **BRIEF** — daily digest of what you saved, with synthesis across captures and a weekly thread |
| - **REVIEW** — spaced repetition (SM-2) targeting specific claims from your own notes, not generic flashcards |
| - **GRAPH** — visual map of how your ideas connect via embedding similarity |
|
|
| ## How it works |
|
|
| 1. You capture a note, URL, or image |
| 2. **NVIDIA Nemotron-Mini-4B** extracts the core insight, classifies intent, generates tags and recall questions |
| 3. **Qwen2.5-VL-7B** handles image captures — describe a whiteboard, diagram, or screenshot |
| 4. **BGE-base-en-v1.5** embeds summaries into a 768-dim vector space |
| 5. Related captures link automatically via cosine similarity |
| 6. The surface engine resurfaces what you should revisit, weighted by intent and time |
|
|
| ## Tech |
|
|
| - **LLM**: `nvidia/Nemotron-Mini-4B-Instruct` via HF Transformers + ZeroGPU |
| - **Vision**: `Qwen/Qwen2.5-VL-7B-Instruct` for image capture |
| - **Embeddings**: `BAAI/bge-base-en-v1.5` (768-dim, top MTEB retrieval) |
| - **Backend**: FastAPI + SQLite (persistent at `/data/mind.db`) |
| - **Frontend**: React + TypeScript + Tailwind CSS |
|
|
|
|
|
|
| ## |
| # Mycelium |
|
|
| **A local-first personal knowledge agent that captures, connects, and surfaces what matters — when it matters.** |
|
|
|  |
|
|
| 🚀 **[Live demo](https://huggingface.co/spaces/build-small-hackathon/mycelium)** · 📹 **[Demo video](https://www.youtube.com/watch?v=Kr7LxRm0JBs)** · 📓 **[Field Notes](https://huggingface.co/blog/build-small-hackathon/mycelium)** |
|
|
| --- |
|
|
| ## The Problem |
|
|
| Everyone saves things. Nobody revisits them. Screenshots, bookmarks, notes-to-self — all gone dark in a week. The capture habit exists. The recall loop doesn't. |
|
|
| Mycelium fixes the recall loop. |
|
|
| --- |
|
|
| ## What It Does |
|
|
| - **Capture** — notes, links, images. Each processed by a local LLM into a structured summary with intent classification (`learn` / `act` / `reference` / `ephemeral`) and semantic tags. |
| - **ASK** — semantic search across your knowledge base with LLM synthesis, gap analysis, Feynman self-testing, and learning arc. |
| - **BRIEF** — daily digest with synthesis across captures and a weekly thread. |
| - **REVIEW** — spaced repetition (SM-2) targeting specific claims from your own notes. |
| - **GRAPH** — visual map of how your ideas connect via embedding similarity. |
|
|
| --- |
|
|
| ## Quick Start |
|
|
| ### Prerequisites |
|
|
| - Python 3.11+ |
| - Node 18+ |
| - [LM Studio](https://lmstudio.ai) — for local LLM inference |
|
|
| ### 1. Clone and install |
|
|
| ```bash |
| git clone https://github.com/ajit3259/Mycelium |
| cd Mycelium |
| python -m venv .venv |
| source .venv/bin/activate # Windows: .venv\Scripts\activate |
| pip install -r requirements.txt |
| ``` |
|
|
| ### 2. Set up LM Studio |
|
|
| 1. Download [LM Studio](https://lmstudio.ai) |
| 2. Load a **chat model** — recommended: `google/gemma-3-4b-it` or any instruction-tuned model under 8B |
| 3. Load an **embedding model** — recommended: `BAAI/bge-base-en-v1.5` (search it in LM Studio's Discover tab, download the GGUF version) |
| 4. Start the Local Server (default: `http://localhost:1234`) |
|
|
|
|
| Repace LM with |
|
|
| Set up GGUF Llama Server |
|
|
| 1. Start your GGUF chat model server |
|
|
| Example: |
|
|
| llama-server \ |
| -m model.gguf \ |
| --host 0.0.0.0 \ |
| --port 8080 |
|
|
| API: |
| http://localhost:8080/v1 |
|
|
| 2. (Optional) Start an embedding model server |
|
|
| If embeddings are required, run a BGE embedding GGUF: |
|
|
| llama-server \ |
| -m bge-base-en-v1.5-q8_0.gguf \ |
| --embedding \ |
| --port 8081 |
| |
| Embedding API: |
| http://localhost:8081/v1 |
| |
| 3. Update application settings |
| |
| Chat API: |
| http://localhost:8080/v1 |
| |
| Embedding API: |
| http://localhost:8081/v1 |
| |
| ### 3. Initialize the database |
| |
| ```bash |
| python -c "from db import init_db; init_db()" |
| ``` |
| |
| Optionally seed with 22 example captures to explore the UI: |
| ```bash |
| python seed.py |
| ``` |
| |
| ### 4. Start the backend |
| |
| ```bash |
| uvicorn main:app --port 8000 |
| ``` |
| |
| ### 5. Build the frontend |
| |
| ```bash |
| cd frontend |
| npm install |
| npm run build # outputs to ../static/, served by FastAPI |
| ``` |
| |
| Open [http://localhost:8000](http://localhost:8000). |
| |
| > **Dev mode (hot reload):** `npm run dev` in the frontend folder — runs on port 5173 with proxy to backend. |
| |
| --- |
| |
| ## Configuration |
| |
| All config is in `config.py` and can be overridden with environment variables: |
| |
| | Variable | Default | Description | |
| |---|---|---| |
| | `LM_STUDIO_URL` | `http://localhost:1234/v1` | LM Studio server URL | |
| | `LM_MODEL` | auto-detected | Chat model name (leave blank to use whatever is loaded) | |
| | `EMBED_MODEL` | `BAAI/bge-base-en-v1.5` | Sentence-transformer embedding model | |
| | `DB_PATH` | `./mind.db` | SQLite database path | |
| | `UPLOADS_DIR` | `./uploads` | Directory for uploaded images | |
|
|
| --- |
|
|
| ## How It Works |
|
|
| ``` |
| Capture → LLM enrichment → Embed → Connect → Surface → Review |
| ``` |
|
|
| 1. You capture a note, URL, or image |
| 2. LM Studio extracts summary, tags, intent, claims, and a recall question |
| 3. `BAAI/bge-base-en-v1.5` embeds the summary into a 768-dim vector |
| 4. Related captures link automatically via cosine similarity (rank-based, top 2) |
| 5. Surface engine resurfaces captures by intent + recency scoring |
| 6. SM-2 spaced repetition schedules review of what you should remember |
|
|
| --- |
|
|
| ## Project Structure |
|
|
| ``` |
| . |
| ├── main.py # FastAPI app — all API endpoints |
| ├── lm.py # Unified LLM interface (LM Studio + HF Transformers) |
| ├── db.py # SQLite schema + all queries |
| ├── surface.py # Intent-weighted surfacing + scoring |
| ├── config.py # Config — auto-detects HF Spaces vs local |
| ├── seed.py # Optional: seed 22 example captures |
| ├── requirements.txt |
| ├── FIELD_NOTES.md # Build post-mortem |
| ├── frontend/ # React + TypeScript + Vite + Tailwind CSS |
| │ └── src/ |
| │ ├── App.tsx |
| │ ├── api.ts |
| │ ├── types.ts |
| │ └── components/ |
| │ ├── CaptureBar.tsx # Note / Link / Image capture |
| │ ├── Feed.tsx # Recent captures |
| │ ├── AskScreen.tsx # Semantic search + synthesis |
| │ ├── BriefScreen.tsx # Daily digest |
| │ ├── ReviewScreen.tsx # Spaced repetition |
| │ ├── GraphView.tsx # Knowledge graph |
| │ └── Card.tsx # Shared capture card |
| └── static/ # Built frontend (served by FastAPI) |
| ``` |
|
|
| --- |
|
|
|
|
