I built a tool that maps brain activation responses to creative content, here's what I learned
Summary
Neural Lens is a tool that maps brain activation responses to creative content using Meta's Tribe v2 model, Claude API, and Hugging Face, providing neurological data on how content resonates with viewers beyond clicks and views.
Similar Articles
I took Meta's TRIBE v2 brain model and made it watch YouTube in real time
A developer built a real-time AI character that watches YouTube videos and reacts using Meta's TRIBE v2 brain model to predict cortical responses, wrapping the neural signal into a voiced 3D avatar that comments on content.
@AnneliesGamble: https://x.com/AnneliesGamble/status/2066949973749755919
An exploration of why mapping the brain's connectome is valuable, arguing that unlike AI systems where design is in code outside weights, brains must encode all design physically, making architecture the key to understanding.
@garrytan: GBrain now can use the embeddings to find you contrarian ideas using 'brainstorm' and 'brainstorm with lsd mode (latera…
Researchers at OpenCollider demonstrated that context engineering techniques can boost originality, and now GBrain uses embeddings to help users find contrarian ideas with its 'brainstorm' and 'brainstorm with lsd mode' features.
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli
TRIBE v2 is a new predictive foundation model designed to understand how the human brain processes complex stimuli.
I built a tool that shows you what GPT-2 is "thinking" in real-time as it generates 3D graph of concept activations per token [R]
A developer built AXON, a tool that visualizes GPT-2's internal concept activations as a live 3D force graph using Sparse Autoencoders, allowing users to see interpretable features firing before token generation.