#go#anticensorship#dns#network#proxy#reality#shadowsocks#socks5#tls#trojan#tunnel#utls#vision#vless#vmess#vpn#wireguard#xhttp#xray#xtls#xudp
Project X offers powerful network tools like Xray-core and REALITY, built on the efficient XTLS protocol that improves speed and security by reducing unnecessary encryption. It features advanced routing and fallback systems to keep your internet traffic safe and uninterrupted, ideal for streaming or video calls. The project is open-source under Mozilla Public License 2.0, encouraging community contributions to keep it evolving. You can easily install it on various platforms using official scripts, Docker, or one-click setups, and use many supported GUI clients on Windows, Linux, Android, iOS, and routers. This flexibility and strong security help you optimize and protect your network experience.
https://github.com/XTLS/Xray-core
#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