#javascript#123pan#139_cloud#189_cloud#ali_netdisk#aliyun_drive#aria2#baidu#baidu_netdisk#baidunetdisk#baiduyun#motrix#quark_netdisk#tampermonkey#tampermonkey_script#tampermonkey_userscript#tianyi_netdisk#uc_netdisk#userscript#xunlei_netdisk#yidong_netdisk
LinkSwift is a browser script that helps you quickly get direct download links for files stored on popular Chinese cloud services like Baidu, Alibaba, 123, and others—saving you time and making downloads easier without needing to visit each service’s website separately. It also improves the look of these cloud storage pages and adds extra features, such as support for different download tools and customizable themes. The main benefit is convenience: you can manage and download your cloud files faster, with a nicer interface, all from your browser. Just install the script using a tool like Tampermonkey, and it works on Chrome, Edge, and other major browsers.
https://github.com/hmjz100/LinkSwift
#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