An experiment in making music with math and Gemini 4 Argon, very much inspired by AA's excellent work and @MazQmusic as well.
All made using an agent in Antigravity CLI.
--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
open-sourced 160+ sound visualization experiments across 25+ categories. waveforms, spectrums, matrix displays, particles, and rhythm. all made while building iwrzwr, a sound app coming soon.
play with them live, steal the code, make them yours 💗
kagan.in/iwrzwr/visual-archi…
For centuries, music software taught the world one way to be in tune.
We built one that listens to all of them.
IntonaLab. Coming soon. 🌏 #IntonaLab
--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
Backpropagation by hand ✍️ ~ 11 steps walkthrough below
Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time.
So I drew and calculated one entirely by hand.
Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias.
= 1. Given =
A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0].
= 2. Backprop gradient cells =
Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first.
= 3. Layer 3 softmax =
We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired.
= 4. Layer 3 weights and biases =
Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together.
= 5. Layer 2 activations =
We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward.
= 6. Layer 2 ReLU =
Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else.
= 7. Layer 2 weights and biases =
We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up.
= 8. Layer 1 activations =
Let us multiply dL/dz2 by W2.
= 9. Layer 1 ReLU =
We apply the same gate again, now on a1.
= 10. Layer 1 weights and biases =
Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient.
= 11. Update =
We subtract, and the network has learned. In practice a learning rate scales this step.
The gradients:
dL/dz3 = [0.5, -0.5, 0]
dL/da1 = [1, -2, 2, -1]
dL/dz1 = [0, -2, 2, -1]
The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning.
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My past work has involved using Brain-Computer Interface (BCI) systems to play music and stage live performances.
Perhaps my current location doesn't offer many opportunities, as it is rife with patronage and cronyism things I lack.
#AI #brainInterface
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Calling public AI infrastructure “communist” is not analysis. The real question is whether public investment produces public capability or merely socializes the cost of privately accumulated power.
Some observations on Kimi:
1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run.
2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China.
3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex.
4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business.
5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this.
6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.
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--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
🎶How can we learn to follow along to a music performance in a score without direct supervision?🎶
Introducing FuSiLi (Fused Sinkhorn-Localized Similarity)!
A multimodal contrastive learning approach that learns precise local alignments using only global supervision.
🧵
--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
Today we’re releasing SEMILLA.AI v2, a totally new interface reconceptualization from the original SEMILLA.AI v1, released in 2023.
Inverse live coding to music to code
open for posibilities on LLM model.
🎼 Can we turn music into readable code?
Excited to share Decomposer, a post-training framework for symbolic music decompilation!
Given MIDI, Decomposer recovers an executable program in Strudel that reconstructs the music while exposing patterns, harmony, rhythm, and voices as editable code.
🧵 [1/5]
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That fucking awesome .
I started working full time on Anukari in February 2023, and the road from blank code editor to something I’m excited to call 1.0 has been full of surprises, successes, flops, and redemptions… but the most rewarding part about the journey has been the people it has connected me with. Users, collaborators, and people who simply think it’s cool.
There are few things I enjoy more than seeing the weird contraptions that people build or hearing their stories about the discoveries they make while using Anukari.
Thank you to everyone who’s followed, supported, contributed or even just offered encouragement along the way. Anukari wouldn’t be what it is without you. I hope you enjoy 1.0 and everything that the future brings for Anukari!
#synthesizers #vst #launchday
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--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
Presented at ADC 2026 Japan today!
Exploring interactive machine learning tools for music generation models.
This demo shows a personalized semantic space (🐶 ↔ 🐱) built from user examples and used to navigate and morph sounds.
More experiments coming soon!!!
#ADC #audiodevcon
--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
I just released this open source project built on @ACEStep_Music. DEMON: Diffusion Engine for Musical Orchestrated Noise. It lets you play ACEStep like a musical instrument, remixing songs and loops with feedback that approaches real-time.
Its essentially StreamDiffusion but instead of Stable Diffusion it is ACEStep1.5, and instead of images it is full songs. It runs on 30/40/5090. Built with @DaydreamLiveAI team, testing, and building the demo. We are hosting it if you want to try it without installing. For full details, links, and writeup please see the pinned project page.
--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
I built a cymatics-inspired sand simulation where sound shapes grains into patterns. Watching chaos organize itself never gets old.
--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
been such a dream to finally make this happen with @OpenAIDevs codex.
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come build :
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--₮ØӾł₵₣ł₵₳₮łØ₦-- retweeted
Build X: Season 2 raised the bar.
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Skills Arena:
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More winner announcements to follow this week. Watch this space.
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Watch the full demo here: unbox-dashboard.pages.dev/mi…
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