Simon Willison’s Weblog

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Announcing Deno 2. The big focus of Deno 2 is compatibility with the existing Node.js and npm ecosystem:

Deno 2 takes all of the features developers love about Deno 1.x — zero-config, all-in-one toolchain for JavaScript and TypeScript development, web standard API support, secure by default — and makes it fully backwards compatible with Node and npm (in ESM).

The npm support is documented here. You can write a script like this:

import * as emoji from "npm:node-emoji";
console.log(emoji.emojify(`:sauropod: :heart:  npm`));

And when you run it Deno will automatically fetch and cache the required dependencies:

deno run main.js

Another new feature that caught my eye was this:

deno jupyter now supports outputting images, graphs, and HTML

Deno has apparently shipped with a Jupyter notebook kernel for a while, and it's had a major upgrade in this release.

Here's Ryan Dahl's demo of the new notebook support in his Deno 2 release video.

I tried this out myself, and it's really neat. First you need to install the kernel:

deno juptyer --install

I was curious to find out what this actually did, so I dug around in the code and then further in the Rust runtimed dependency. It turns out installing Jupyter kernels, at least on macOS, involves creating a directory in ~/Library/Jupyter/kernels/deno and writing a kernel.json file containing the following:

{
  "argv": [
    "/opt/homebrew/bin/deno",
    "jupyter",
    "--kernel",
    "--conn",
    "{connection_file}"
  ],
  "display_name": "Deno",
  "language": "typescript"
}

That file is picked up by any Jupyter servers running on your machine, and tells them to run deno jupyter --kernel ... to start a kernel.

I started Jupyter like this:

jupyter-notebook /tmp

Then started a new notebook, selected the Deno kernel and it worked as advertised:

Jupyter notebook running the Deno kernel. I run 4 + 5 and get 9, then Deno.version and get back 2.0.0. I import Observable Plot and the penguins data, then render a plot which shows as a scatter chart.

import * as Plot from "npm:@observablehq/plot";
import { document, penguins } from "jsr:@ry/jupyter-helper";
let p = await penguins();

Plot.plot({
  marks: [
    Plot.dot(p.toRecords(), {
      x: "culmen_depth_mm",
      y: "culmen_length_mm",
      fill: "species",
    }),
  ],
  document,
});