Vellum Editor — A Showcase

Vellum Editor Showcase

Some things you can create, all in one place.

package main

import "fmt"

func isOdd(n int) bool {
	return n%2 != 0
}

func main() {
	fmt.Println(isOdd(3)) // true
}

fn divide(a: i32, b: i32) -> Result<i32, String> {
    if b == 0 {
        Err("division by zero".to_string())
    } else {
        Ok(a / b)
    }
}

fn main() {
    match divide(10, 2) {
        Ok(v) => println!("{}", v),
        Err(e) => println!("Error: {}", e),
    }
}

1. Text Formatting

You can make text bold, italic, bold and italic, strikethrough, and inline code.

Mix them: The quick brown fox jumped over the slow lazy dog.

2. Blockquote

Knowledge is not information — it is information understood in context.

The Vellum editor is designed for structured thinking, not just note-taking.

3. Inline Math

Einstein: E = mc^2 — Euler: e^{i\pi} + 1 = 0 — Gaussian: \int_{-\infty}^{\infty} e^{-x^2}\,dx = \sqrt{\pi}

The normal distribution \mathcal{N}(\mu, \sigma^2) has PDF f(x) = \frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}.

A matrix A \in \mathbb{R}^{m \times n} is invertible iff \det(A) \neq 0, iff all eigenvalues \lambda_i \neq 0.

The gradient of f: \mathbb{R}^n \to \mathbb{R} is \nabla f(\mathbf{x}) = \left(\frac{\partial f}{\partial x_1}, \ldots, \frac{\partial f}{\partial x_n}\right).

4. Block Math

5. Tables

Algorithm Complexity

AI Model Comparison

6. Bullet List

• Hilbert Space — an infinite-dimensional inner product space that is complete with respect to the norm

• Eigenvector — a non-zero vector v such that Av = \lambda v for some scalar \lambda

• Fourier Transform — decomposes a function into its constituent frequencies

  • Used in signal processing, quantum mechanics, and image compression

  • The discrete version (DFT) is computed efficiently via the FFT algorithm in O(n \log n)

• Gradient Descent — iterative optimization algorithm that moves in the direction of steepest descent

• Backpropagation — efficient algorithm for computing gradients in neural networks via the chain rule

7. Ordered List

1. Define the problem — clearly state what you are trying to solve and what success looks like

2. Gather evidence — collect data, literature, and prior art relevant to your question

3. Form a hypothesis — propose a falsifiable explanation for the phenomenon

4. Design an experiment — create a controlled test that can confirm or refute your hypothesis

5. Analyze results — apply statistical methods and interpret findings honestly

6. Draw conclusions — summarize what the evidence supports, and what remains uncertain

7. Communicate findings — write up results with enough detail for others to replicate

8. Task List

• ☑

  Set up the Vellum editor with math support

• ☑

  Add dollar-sign delimiter support for paste from chat

• ☑

  Fix mixed-content paste for HTML and math

• ☑

  Add syntax-highlighted code blocks

• ☐

  Add collaborative editing

• ☐

  Implement Vellum version history

• ☐

  Build Libram marketplace with installable packages

9. Vector Calculus Identities

The gradient of f: \mathbb{R}^n \to \mathbb{R} is \nabla f(\mathbf{x}) = \left(\frac{\partial f}{\partial x_1}, \ldots, \frac{\partial f}{\partial x_n}\right).

10. Quick Reference

Created with Viewert — the AI-native knowledge layer.