Embedding Explorer
Navigate the semantic space where meaning becomes geometry. Pan, zoom, and discover what's near what.
Try these clusters:
What am I looking at?
This is a 2D projection of an embedding space. In AI, embeddings are vectors — lists of numbers — that represent the meaning of words, concepts, or any kind of data. When you plot these vectors, things that are semantically similar end up close together.
The ~200 points here span four categories: music genres, emotions, tech concepts, and fun/experiential items. Their positions are designed to reflect real semantic relationships:
- Electronic, house, and techno cluster tightly because they share sonic DNA.
- Jazz, blues, and soul sit near each other — and near positive emotions like joy and contentment.
- Metal, punk, and grindcore cluster far from ambient and classical.
- "Meditation" lives near "serenity" and "peace" across category boundaries.
- ML/AI terms (transformer, attention, LLM) form their own dense cluster, separate from web/infra tech.
In real embedding models (like those powering LLMs), these spaces have hundreds or thousands of dimensions. This 2D view is a simplified illustration — but the clustering principle is exactly how modern AI understands meaning.