#csharp#cross_platform#csharp#vpn#xamarin
VpnHood is a free, open-source VPN that helps you stay private online and access blocked content, even on networks with strict firewalls and deep packet inspection[1][5]. It works on Windows and Android, is easy to install, and runs fast without needing special network knowledge or admin rights[1]. You can choose which apps or countries use the VPN, and it supports both IPv4 and IPv6. Because it’s open source and has passed a security audit, you can trust that your data is safe and the software is transparent[1]. This means you get better privacy, can bypass restrictions, and have more control over your internet connection, all without cost or complexity[1][3].
https://github.com/vpnhood/VpnHood
#DL
📱
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