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Posts tagged with Artificial Intelligence

Using WASM as a Sandbox for AI agents

Artificial intelligence (AI) agents are becoming increasingly powerful, but with great capability comes the need for strong isolation and security guarantees. WebAssembly (WASM) offers a lightweight, portable, and sandboxed execution environment that can protect host systems while still delivering high performance. In this post, we explore how WASM can serve as an effective sandbox for AI agents, discuss the technical advantages, outline common architectures, and highlight real‑world use cases.

Why Choose WebAssembly for AI Sandboxing?

  • Platform independence: WASM modules run consistently across browsers, servers, edge devices, and even embedded systems without modification.
  • Strong security model: The runtime enforces memory
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Using Einops with Pytorch

When working with deep learning models in PyTorch, tensor reshaping, rearranging, and reduction operations are inevitable. While torch.view, torch.permute, and torch.reshape get the job done, they often produce code that is hard to read and error‑prone. Einops (Einstein‑Notation for tensors) offers a concise, expressive, and readable alternative that integrates seamlessly with PyTorch. In this post we’ll explore why Einops is valuable, how to install it, and walk through practical examples that demonstrate its power.

What Is Einops?

Einops is a lightweight library that provides three core functions:

  • rearrange: Generalized permutation and reshaping.
  • reduce: Aggregation
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Unraveling self-attention vs cross-attention in Transformers

If you have worked with Transformers—whether BERT, GPT, or the original Encoder-Decoder architecture—you are intimately familiar with the concept of "Attention." The equation is arguably the most famous in modern NLP:

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

However, strictly memorizing the formula often masks the architectural nuances. While the mathematical operation is identical, the source of the inputs determines whether you are performing Self-Attention or Cross-Attention.

This article explores the mechanical and semantic differences between these two mechanisms, targeted at practitioners who understand the basics of deep learning.

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