We're one step closer to A2 today, and I've released an update to NeuralAmpModelerCore, version 0.5.0: https://github.com/sdatkinson/NeuralAmpModelerCore/releases/tag/v0.5.0 Builders interested in how A2 works should aim to support core version 0.5.0 or later. Work isn't entirely compelte yet-- while 0.5.0 will be able to process A2 models correctly , optimized implementations are in development and will be released soon in a follow-up version (0.5.1). If you just want to und

Progress on Architecture A2 has been coming along well, and I'm pretty happy with the results that we've gotten out of Stage 4 of the work (optimizing A2). With that, development will enter the fifth and final stage soon: listening tests . If you haven't already, please sign up to participate by filling out this Google Form . Please also share it with people you know--the more responses we get, the better I can understand how A2 is doing! Thanks for your patience; I'm re

This post is part of a series on Architecture A2 . I've been working on designing NAM's new default architecture, "A2". In this blog post, I wanted to give stakeholders some insight into what I've seen so far in Stage 4 ("Optimize A2", from the original blog post linked above). The work isn't done yet--but I do want to share some information and how it's influencing the way I'm thinking. If you're a builder who's looking to prioritize your work, then this may be useful. Test

Update 2026-02-12: The test models have been updated to "v4" to fix some identified issues. Please re-download the latest if you haven't begun testing. If you've already sent me results, don't worry about it--I'll note the discrepancies and account for them. The core repo has also been updated to v0.4.0.rc3 Progress is coming along on work to develop Architecture A2 , NAM's update to the "standard WaveNet" and its lite/feather/nano variations (retroactively, "A1"). NeuralAmpM