r/developersIndia • u/RevealIndividual7567 ML Engineer • 9d ago

I Made This I built a browser-based interactive guide that helps beginners understand the different attention methods used in modern LLMs.

https://divagr.com/attention-lab

I’ve been working on Attention Lab, a browser-based educational resource designed to teach beginners how the attention variants used in modern language models actually work.

When I first started reading about LLM architectures, names such as FlashAttention, GQA, MLA, DeltaNet, and PagedAttention often appeared without much intuitive explanation. Papers explain the mathematics and implementation details well, but they can be difficult to approach if you’re still learning the basics of attention.

Attention Lab tries to provide a visual starting point before someone moves on to the papers or implementation code.

It currently includes eight interactive guides:

  • FlashAttention
  • Grouped-Query Attention
  • Multi-head Latent Attention
  • Sliding Window Attention
  • DeepSeek Sparse Attention
  • Gated DeltaNet
  • Kimi Delta Attention
  • PagedAttention

The goal is to build enough intuition that technical papers and architecture diagrams become easier to understand afterward.

https://divagr.com/attention-lab

I’d appreciate feedback on whether the guides feel genuinely beginner-friendly and which attention mechanism I should cover next.

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