Neura saysTransformers ("Attention Is All You Need") dropped recurrence for pure attention — and changed everything.
The 2017 transformer used self-attention alone — no recurrence — so every position attends to every other in parallel. That parallelism made training on massive data feasible (RNNs were sequential), and the architecture scaled astonishingly well. Every modern LLM (GPT, Claude, Gemini) is a transformer. It's arguably the most important ML architecture of the era.
Power-ups you unlock
Pure attention, no recurrence
All positions attend in parallel → fast training
Scales remarkably with data/compute
The basis of every modern LLM
The Overfit Ogre attacks — common mistakes
Thinking transformers are only for text (also vision, audio)
Underestimating the role of parallelism
Confusing the architecture with a specific model
Boss battleExplain why removing recurrence let transformers train at scale.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>RNN: process token by token (sequential)
transformer: all tokens attend in parallel
→ train on internet-scale data → LLMs</pre></body></html>