r/singularity loved the premise immediately: a 13B model trapped at a 1930 knowledge cutoff. The upvotes came from the mix of novelty and real research value, because Talkie is not just a gimmick chat partner but a clean lab for studying what models learn without the modern web.
r/MachineLearning did not reward this post for frontier performance. It took off because a 7.5M-parameter diffusion LM trained on tiny Shakespeare on an M2 Air made a usually intimidating idea feel buildable.
r/MachineLearning treated this less like a finished breakthrough and more like a serious challenge to the current assumptions around large-scale spike-domain training. The April 13, 2026 post reported a 1.088B pure SNN language model reaching loss 4.4 at 27K steps with 93% sparsity, while commenters pushed for more comparable metrics and longer training before drawing big conclusions.
A research-oriented post on r/MachineLearning claimed that a pure spiking neural network language model could reach 1.088B parameters from random initialization before budget limits ended the run.
A March 19, 2026 Hacker News post about NanoGPT Slowrun reached 162 points and 43 comments at crawl time. Q Labs says an ensemble of 1.8B-parameter models trained on 100M tokens matched a baseline that would normally require 1B tokens.