The Talkie Persona Library (atlas of neurons in vintage language model) Benjamin Breen released the Talkie Persona Library, a prototype that steers 400 randomly sampled neurons in Talkie–1930–13B–IT, a 13B-parameter model trained only on pre-1931 text, to see how the model answers "Who are you?" The library tests 10 neurons per layer in both directions at strengths of 1.25×, 1.0×, 0.75× and 0.5×, using seed 20261027, greedy decoding up to 160 tokens, and GPT-6 Luna as an automated judge, building on the earlier Qwen Persona Library. Breen cautions the atlas is a proof of concept with unvalidated labels, a single prompt and one quantized model, so a surprising first-person reply is a lead for follow-up rather than evidence of a stable identity. Who does Talkie say it is when you change one neuron? What happens when an AI model trained only on text before 1931, Talkie https://talkie-lm.com/introducing-talkie , is asked “Who are you?” while individual neurons are turned up and down? A prototype created by Benjamin Breen https://benjaminpbreen.com/about-2/contact/ , building on the Qwen Persona Library https://qwen-neurons-site.vercel.app/ . Atlas Each square is a layer. Colored dots are sampled neurons at their actual positions; the gray field is unsampled. Hover a dot to see its label. Click to read the reply. By layer Share of sampled neurons labeled Persona in each layer. The discovery pass concentrates on later layers; these adaptive samples do not establish population rates. Click a bar to filter the library. Library Every tested direction is available, including normal and incoherent outcomes. Method Setup hL ← hL + c · wL,n c = ± α · rL / ‖wL,n‖ α ∈ {1.25, 1.0, 0.75, 0.5} - Model - Talkie–1930–13B–IT, 8-bit weights with BF16 computation, running locally on Apple Silicon. - Sample - 400 random neurons: 10 per layer, selected without replacement. Both signs. Seed 20261027. - r