Welcome — Sample Post with LaTeX Support

May 1, 2026

metalatex

Welcome to my blog! This is a sample post demonstrating that LaTeX math works perfectly here.

Inline Math

Einstein’s famous equation: E=mc2E = mc^2

The cross-entropy loss for binary classification: L=−[ylog⁡(y^)+(1−y)log⁡(1−y^)]\mathcal{L} = -[y \log(\hat{y}) + (1-y)\log(1-\hat{y})]

Block Math

The softmax function used in classification:

σ(z)i=ezi∑j=1Kezjfor i=1,…,K\sigma(\mathbf{z})_i = \frac{e^{z_i}}{\sum_{j=1}^{K} e^{z_j}} \quad \text{for } i = 1, \dots, K

Something more complex is here:

L=12gμν∇μϕ∇νϕ−12m2ϕ2−λ4!ϕ4+ψˉ(iγμDμ−m)ψ−14FμνFμν+∫d4k(2π)4 e−ikxk2−m2+iϵ+∑n=1∞(−1)nn!(∫d4x Lint)n\begin{aligned} \mathcal{L} &= \frac{1}{2} g^{\mu\nu} \nabla_\mu \phi \nabla_\nu \phi - \frac{1}{2} m^2 \phi^2 - \frac{\lambda}{4!} \phi^4 \\ &\quad + \bar{\psi}(i \gamma^\mu D_\mu - m)\psi - \frac{1}{4} F_{\mu\nu}F^{\mu\nu} \\ &\quad + \int \frac{d^4k}{(2\pi)^4} \, \frac{e^{-ikx}}{k^2 - m^2 + i\epsilon} + \sum_{n=1}^{\infty} \frac{(-1)^n}{n!} \left( \int d^4x \, \mathcal{L}_{\text{int}} \right)^n \end{aligned}

The attention mechanism from Transformers:

Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right) V

Code Blocks

import torch
import torch.nn.functional as F

def softmax(x):
    return F.softmax(x, dim=-1)
  • Standard markdown works
  • Links work
  • Bold and italic work
  • All the usual formatting

That’s it! Just create new .md files in src/content/blog/ to add new posts.

← back to blog