Alternatives
Products that do what BraidForge SU(3) does
AI stress tests for topological qutrit gates
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2025 · matthieulc.com
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Apr 2026 · github.com
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We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…
2025 · tinytpu.com
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A 3.16M-parameter INT4 transformer running entirely in the on-chip memory of a Xilinx Kria KV260. Zero DRAM in the token loop, 59,965 tok/s on the fabric, bit-exact. Chat with it live.
27d ago · mikeayles.com
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We’ve just released an open-source library for solving the Maximum Independent Set (MIS) problem with neutral atom quantum computing, running on both quantum processing units (QPUs) and classical hardware, thanks to emulators. This project is the result of collaboration between Pasqal, academic researchers, and industry partners, aiming to make it practical to experiment with quantum approaches to hard combinatorial optimization tasks. The MIS problem appears in real-world scenarios like scheduling, resource allocation, and network optimization, areas where classical solvers often struggle…
2025
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Local, gradient-free neuro-symbolic memory engine combining Hyperdimensional Computing (HDC/VSA), Hebbian plasticity, and graph triples for offline AI. - roandejager/Hillock
7d ago · github.com
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Hi! I built this to teach myself how a quantum circuit simulator works. It's about 1000 lines of Python, aiming to be understandable more than being fast or robust. It's not meant to compete with anything else - just a personal project to really get clear on the under-the-hood theory of qubits and quantum gates.
2024 · github.com
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2015 · github.com
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2018 · github.com
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The model uses a 1024-dimensional complex Hilbert space with 32 layers of programmable Mach–Zehnder meshes (Reck architecture) and derives token probabilities directly via the Born rule. Despite using only unitary operations and no attention mechanism, a 1024×32 model achieves coherent TinyStories generation after < 1.8 hours of training on a single consumer GPU. This is Part 1 - the next step is physical implementation with $50 of optics from AliExpress.
Nov 2025 · zenodo.org
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We wanted to test if a smaller model like GPT-4.1-mini could beat its bigger brother 4.1 at the game Tic-Tac-Toe using only context engineering. We put them in a 100-game tournament. For the smaller model, we gave it a few examples of winning moves from past games right before it made its own move. The results were clear. Without the examples, the smaller model struggled against GPT-4.1. With the examples, its effectiveness increased by nearly 200%, and it consistently won. It's a simple demonstration, but it shows that a smaller, faster model with good, timely examples can outperform a more…
2025 · github.com
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Apr 2026 · github.com
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Hi HN, We're proud to announce the release of the Quantum Evolution Kernel! It's still an early version, so feedback is very much welcome. # What is it? Quantum evolution kernel is an open-source library designed for anyone interested in applying quantum computing to graph machine learning -- and you don’t even need a quantum computer in your living room to start using it! It has a wide range of graph machine learning applications, including prediction of molecular toxicity, as shown in the tutorial. # Why is it exciting? Quantum computing has huge potential, but it needs to be accessible…
2025 · github.com
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Kimi K3 has 2.78 trillion parameters and ships as 1.42 TB of weights. It clearly does not fit in the memory of a laptop. But K3 is a Mixture-of-Experts model. For each token, only a small fraction of its 896 experts per layer is activated. That changes the problem: the entire model does not need to be resident in RAM, as long as the weights required by each token can be reached quickly enough. We built WASTE — the Weight-Aware Streaming Tensor Engine — to explore that idea. WASTE keeps the dense, repeatedly used part of the model resident in memory, stores the routed experts in an…
Jul 2026
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Hi guys, I’m excited to share an update on ReproModel, an open-source toolbox designed to streamline the testing and reproduction of machine learning models. I, like many of you, have really struggled with benchmarking and comparing models, from missing code, to opaque experiment parameters slowing the process. I decided to take matters into my own hands, and created a mini-toolbox in my free time to streamline the process. The goal is to reduce the time and effort spent on replicating experiments, enabling researchers to focus on innovation rather than setup. Knowing this task is not an…
2024 · github.com
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This has been a small labour of love for a few days. Just a fun idea I had to create a single qubit quantum circuit simulator with interactive 3D bloch sphere. Check out my blog post linked on the page for further introduction to quantum computing!
2024 · dazkins.com
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