Life & fun · December 23, 2017
Arraymancer, teaching Nim language digit recognition from scratch
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends - mratsim/Arraymancer
Arraymancer, teaching Nim language digit recognition from scratch launched on December 23, 2017 with 9 votes, #950 of 2,482 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 8 years on.
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Arraymancer v0.4, Nim tensor library now with OpenCL and Keras-like API2018 · github.com · ▲11A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends - mratsim/Arraymancer

- GAGPU-Accelerated Digit Recognition with WebGL2016 · erkaman.github.io · ▲135

I built a tensor library from scratch in C++/CUDA2025 · github.com · ▲119Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…

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