Introduction

This is our first post on the research blog. It demonstrates all the content types we support.

Math Equations

Inline math works like this: the famous equation E=mc2E = mc^2 relates energy and mass.

Display equations are also supported:

∇⋅E=ρϵ0\nabla \cdot \mathbf{E} = \frac{\rho}{\epsilon_0}

The softmax function is defined as:

softmax(xi)=exi∑jexj\text{softmax}(x_i) = \frac{e^{x_i}}{\sum_{j} e^{x_j}}

Code Examples

Here is a Python function:

def attention(Q, K, V):
    d_k = Q.shape[-1]
    scores = Q @ K.transpose(-2, -1) / d_k ** 0.5
    weights = torch.softmax(scores, dim=-1)
    return weights @ V

And some TypeScript:

interface Post {
  title: string
  date: string
  author: string
  content: string
}

Images

Below is a placeholder image for testing:

Test placeholder

Blockquotes

"The only way to do great work is to love what you do."

Lists

Key findings from our research:

  • Attention mechanisms scale quadratically with sequence length
  • Sparse attention patterns can reduce this to linear complexity
  • The trade-off between accuracy and efficiency depends on the task
  1. First, compute query and key projections
  2. Then, calculate attention scores
  3. Finally, apply softmax and multiply by values

Conclusion

This post confirms that all content types render correctly on our blog.