The Method.
Mapping human imagination into high-dimensional vector space.
Vector Embeddings
How do you teach a computer to see "cozy"? You don't utilize tags. You utilize math. We pass every image through a neural network (CLIP) that translates pixels into a list of 768 numbers—a vector embedding.
[0.023, -0.154, 0.891, -0.412, 0.005, 0.671, ...]
This array represents the semantic essence of the image. Its colors, its composition, its vibe.
High-Dimensional Space
Imagine a 3D room where all similar objects are floating near each other. Now imagine a room with 768 dimensions. This is where Chipling lives.
In this space, "Sunset" is mathematically close to "Orange," "Warmth," and "Melancholy." When you search, we don't scan database rows; we calculate the distance between your thought and every image in existence.
Curated Discovery
Traditional search engines optimize for exact matches. We optimize for the "adjacent possible." We intentionally introduce randomness and wide-net proximity to surface results that are surprising yet inevitably correct.
We believe the best ideas are found in the periphery.