GoKawiilShow HN tool visualizes attention weights behind LLM token generation
A hobbyist project lets users hover over tokens generated by a transformer language model to see which earlier tokens most influenced each output, by combining attention weights and value vector magnitudes across all heads and layers into a single opacity value. The demo highlights cases like verbatim copying of addresses or dates, showing the source text lighting up strongly when reproduced.