Features of this site

This site is built with SvelteKit and uses Pandoc to convert LaTeX articles to HTML at build time. Here is what is available.

Math

Inline math works with dollar signs. For example, the quadratic formula is $x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}$ and Euler’s identity is $e^{i\pi} + 1 = 0$.

Display math uses double dollars:

$$\nabla \cdot \mathbf{E} = \frac{\rho}{\varepsilon_0}$$

The cross-entropy loss commonly used in NLP:

$$\mathcal{L}(\theta) = -\sum_{i=1}^{N} \log p_\theta(x_i)$$

Code

Code blocks use verbatim environments:

import torch
import torch.nn as nn

class Transformer(nn.Module):
    def __init__(self, d_model: int, nhead: int):
        super().__init__()
        self.attention = nn.MultiheadAttention(d_model, nhead)
        self.norm = nn.LayerNorm(d_model)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        attn_out, _ = self.attention(x, x, x)
        return self.norm(x + attn_out)

Inline code uses texttt, like const x = 42.

Images

Articles can include images from their assets/ folder. The build script copies them to static/articles/<slug>/ so they are served as static files.

Citations

Each article can have its own refs.bib file. Pandoc processes citations automatically via –citeproc.

The Transformer architecture [1] revolutionized natural language processing. For foundational algorithms, see Knuth [2].

Typography

Regular text uses Cascadia Mono. Bold and italic work as expected.

Blockquotes are rendered from the quote environment.

Lists work too:

1. First item

2. Second item

3. Third item

Publications

Site-wide publications are loaded from a BibTeX file (content/refs.bib) and rendered on the publications page. Article-specific references are kept in each article’s own refs.bib.