prismnote 0.4.0

Modern, open-source Jupyter-compatible data science notebook with Rust performance

◆ PrismNote

A fast, modern, open-source data-science notebook. Rust engine · React UI · local-first · AI-native.

License: MIT · Status: Beta (v0.4)

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Overview

PrismNote is a local-first alternative to Jupyter, Zeppelin, and Databricks notebooks. It pairs a Rust + Axum backend and a persistent Python kernel with a modern, VS Code-style React interface. Write Python and SQL, get rich output and live charts, ask a local AI for help, schedule notebooks as jobs, manage git, and ship to the cloud — all from one app that runs entirely on your machine.

• Local-first & private — runs on your machine; local AI via Ollama, no account required.
• Jupyter-compatible — native .ipynb format.
• Batteries included — kernel, SQL, charts, AI, jobs, git, deploy, search, terminal.

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Features

Notebook & execution

• Persistent shared kernel — variables, imports, and functions defined in one cell are available in every later cell (true notebook semantics).
• Per-cell interpreters (magics):
  • %python (default) — runs in the shared kernel.
  • %sql — in-process DuckDB; can query pandas DataFrames defined in other cells.
  • %sh / %bash / !cmd — shell commands.
  • %md / %markdown — rendered markdown.
• Rich output — text, HTML, matplotlib figures (image/png), and pandas DataFrames.
• Chart switcher — view any DataFrame as a Table, Bar, or Line chart.
• Live streamed output over WebSocket while a cell runs.
• Interrupt a running cell (Stop) and restart the kernel from the toolbar/menu.
• Dynamic input widgets — prism.input / slider / select / checkbox render controls that re-run the cell when changed (Databricks dbutils.widgets-style).
• Pretty-printing enabled by default via rich.

AI assistance

Works with local models via Ollama (free, private, offline) or Claude / OpenAI.

• ⌘K in-cell edit — describe a change, review a diff, accept or reject.
• Fix with AI — one click on an errored cell.
• Explain — a teaching-oriented explanation with a contextual tip.
• Inline autocomplete — ghost-text completions when Ollama is connected.
• Teacher persona — any model explains the "why" and suggests tips/tricks.
• Agent panel — Plan/Act assistant that can propose and apply cell edits.

Errors that help

• Natural-language explanations for common Python errors (NameError, KeyError, ModuleNotFoundError, TypeError, ZeroDivisionError, SyntaxError, …).
• In-editor markers highlighting the offending line and column.
• Collapsible full traceback when you want the details.

Workflow: Jobs, Airflow, Git, Deploy

• Jobs — save a whole notebook and run it as a unit, manual / interval / daily, with status and run history (Airflow-style).
• Airflow — a stable run-by-name trigger plus a generated, ready-to-use DAG.
• Source control — init, clone, commit, push, pull, status from the UI.
• Cloud deploy — generated Dockerfile, docker-compose.yml, k8s.yaml, and fly.toml, each with its one-line deploy command.

Workspace & UX

• VS Code-style layout: activity rail, collapsible panels, status bar.
• All panels collapsible, each with independent font +/- controls.
• Responsive — side panels auto-collapse on narrow windows.
• Global search (⌘K), command palette (⇧⌘P), working top menus.
• Integrated Terminal, a Python Console (shares the kernel), and a file browser (works in any browser via a server-side browser).
• Dark/light themes; .ipynb import/export.

Data connectivity

• DuckDB in-process SQL engine.
• Connection scaffolding for 8 cloud warehouses (Snowflake, BigQuery, Redshift, Azure Synapse, Databricks, Athena, Presto, Trino) — see CLOUD_WAREHOUSES.md.

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Architecture

┌──────────────────────────────┐        ┌─────────────────────────────┐
│  React + TypeScript (Vite)   │  HTTP  │      Rust backend (Axum)    │
│  Monaco · Tailwind · zustand │ ─────▶ │  REST API + WebSocket       │
│  cells · panels · AI · jobs  │ ◀───── │  routing, jobs, git, deploy │
└──────────────────────────────┘   WS   └──────────────┬──────────────┘
                                                        │ stdin/stdout (JSON)
                                                ┌───────▼────────┐
                                                │ Persistent      │
                                                │ Python kernel   │
                                                │ (shared globals)│
                                                └─────────────────┘

• Backend: Rust (Axum + Tokio). Spawns one long-lived Python process and talks to it over a line-framed JSON protocol; outputs are Jupyter-style MIME bundles.
• Frontend: React 18 + TypeScript + Vite, Monaco editor, Tailwind CSS.
• Kernel: a single python process with a shared namespace; SIGINT-interruptible, restartable, with matplotlib (Agg) and rich preloaded.

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Getting started

Prerequisites

• Rust (stable) and Node.js 18+
• Python 3.8+ on your PATH with ipykernel:

  pip install ipykernel pandas matplotlib rich duckdb
  

• (optional) Ollama for local AI: https://ollama.com — then ollama pull qwen2.5-coder

Run in development

# 1) backend (port 8000)
cargo run

# 2) frontend (port 5173, proxies /api and /ws to the backend)
cd frontend && npm install && npm run dev

Open http://localhost:5173.

Configure AI (optional)

• Local (Ollama): start Ollama and pick a model in the AI panel.
• Claude/OpenAI: set in the AI settings, or via env:

  export PRISMNOTE_AI_PROVIDER=claude   # or openai / ollama
  export ANTHROPIC_API_KEY=...          # or OPENAI_API_KEY
  

  Settings saved in the UI persist to ~/.prismnote/ai_config.json.

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Deploy

Open Deploy to Cloud (rocket icon) to copy/download ready-to-use artifacts:

docker compose up -d          # Docker
kubectl apply -f k8s.yaml     # Kubernetes
fly launch --copy-config --now# Fly.io

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Keyboard shortcuts

Shortcut	Action
⌘N / ⌘O / ⌘S	New / Open / Save notebook
⌘K	Global search
⇧⌘P	Command palette
⌘K (in a cell)	AI edit
⌘⇧⏎	Run all cells
⇧⏎	Run cell

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Repository layout

crates/server/      Rust backend (api, kernel, jobs, db, ws, …)
frontend/           React app (components, hooks, api clients)
*.md                Architecture & comparison docs

Comparisons: ZEPPELIN_COMPARISON.md · DATABRICKS_COMPARISON.md · NOTEBOOK_COMPARISON_MATRIX.md

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Roadmap

• Distributed compute (Spark) and a catalog/data browser.
• Real-time collaboration (live cursors/co-editing).
• Notebook parameters and multi-notebook job composition.

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License

MIT — see LICENSE.