News
Updates from Jean Technologies.
Emotion Vectors and the Future of Matching
What Anthropic's new interpretability research means for compatibility infrastructure
Anthropic just published landmark research showing that LLMs maintain abstract, causally operative representations of emotion. This is something we have been thinking about for a long time, and it changes how we should build matching systems.
Why General-Purpose Embeddings Fail at Human Matching
Standard embedding models are trained on text similarity. But when the goal is predicting compatibility between people, text similarity is the wrong objective entirely.
Introducing the Embedding Adapter
Switching embedding providers has historically meant re-encoding your entire vector corpus. Today we are releasing infrastructure that eliminates that requirement.
Jean Technologies
We started Jean Technologies to solve a problem we kept seeing across industries: platforms that match people to people, or people to opportunities, using infrastructure designed for document search.
General Personal Embeddings
An exploration of how deep representation learning can be applied to people, and why the logical endpoint is trusted infrastructure for personal embeddings.