// TOPIC
#attention
4 articles
◆◆Intermediate
01The Transformer Block: Residual Stream, MLP, and What Each Part Learns
How attention and MLP sublayers share a residual stream, what factual knowledge each stores, and why this view changes how you debug and fine-tune LLMs.
#llm-internals#attention#fundamentals
15 min◆◆Intermediate
02Self-Attention: The Mechanism That Reads Every Token Against Every Other
How self-attention computes dynamic, context-dependent relevance weights between all token pairs, why it scales quadratically, and what that costs you in production.
#llm-internals#attention#inference
18 min◆Beginner
03Embeddings and positional encoding: how tokens become geometry
How token embedding tables and positional encodings work, why RoPE dominates 2025 models, and what context-window advertising actually hides from you.
#llm-internals#embeddings#fundamentals
15 min◆Beginner
04Transformers Without the Math
Build the mental model every AI engineer needs: how tokens become vectors, why attention is quadratic, what the MLP layers store, and how depth stacks understanding.
#llm-internals#attention#embeddings
14 min