@jacsebl says he got Fable 5.1 to write, by hand, the weights of a 100,000-parameter model that tells real legal citations from fake ones, and that it beat training the model the usual way. @daniel_mac8 calls weight-space programming the next big reasoning...
VQV Signal
A Claude Fable 5.1 user says it hand-wrote the weights of a working 100k-parameter network
@jacsebl says he got Fable 5.1 to write, by hand, the weights of a 100,000-parameter model that tells real legal citations from fake ones, and that it beat training the model the usual way. @daniel_mac8 calls weight-space programming the next big reasoning...
@jacsebl says he got Fable 5.1 to write, by hand, the weights of a 100,000-parameter model that tells real legal citations from fake ones, and that it beat training the model the usual way. @daniel_mac8 calls weight-space programming the next big reasoning leap. Why it matters: If models can write small networks...
Teams using AI at work may want to compare this against current productivity and review workflows.
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
Public Interest components
VQV surfaced this signal because it is recent, relevant to Consumer AI, connected to Scobleizer News Pulse (X).
No login, cookies, social SDKs, or automatic posting.