#c_lang#c#drivers#gpl#hacktoberfest#kernel#operating_system#os#osdev#reactos#win32#win32api#windows#x86
ReactOS is a free, open-source operating system designed to be compatible with Windows applications and drivers, especially those for Windows Server 2003 and later versions. The latest version, 0.4.15, brings major improvements like better USB and driver support, enhanced system stability, 64-bit fixes, and new features in system tools such as Notepad and Paint. It can be tested safely on virtual machines and is ideal for users seeking a Windows-like experience without Microsoft’s software. ReactOS is still in alpha, so it’s best for testing, but it offers a promising alternative for Windows users wanting a free, open-source OS[1][2][3].
https://github.com/reactos/reactos
#DL
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Zeus New Pytorch Ecosystem Tool
Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads.
🖥Github
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Main channel: @repo_science
Coupons: @freecoupons_reposcience
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#dl
Park, Chanwook, Sourav Saha, Jiachen Guo, Hantao Zhang, Xiaoyu Xie, Miguel A. Bessa, Dong Qian, et al. 2025. “Unifying Machine Learning and Interpolation Theory via Interpolating Neural Networks.” Nature Communications 16 (1): 1–12.
https://www.nature.com/articles/s41467-025-63790-8
#dl
A few cool ideas in this model.
Introducing Gemma 3n: The developer guide - Google Developers Blog
https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/
#dl
There is this new lib called scale. One could compile CUDA code to use it on AMD GPU.
https://docs.scale-lang.com/manual/how-to-use/
I don't know who is more pissed off, NVidia or AMD.
#dl
This repo is really nice.
yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models
https://github.com/yuanchenyang/smalldiffusion
#dl
Google & USC benchmarked a prompt based forecasting method, and the results are amazing.
Cao D, Jia F, Arik SO, Pfister T, Zheng Y, Ye W, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for time series forecasting. arXiv [cs.LG]. 2023. Available: http://arxiv.org/abs/2310.04948