#javascript#cloud#cloud_os#cloud_storage#desktop#desktop_environment#dropbox#good_first_issue#gui#javascript#nas#open_source#operating_system#os#osjs#puter#remote_desktop#storage#web_desktop#web_os#webtop
Puter is a privacy-first personal cloud that lets you store files, apps, and games securely. You can access everything from anywhere at any time, making it very convenient. It's like a personal computer in the cloud, and you can use it on any device—Windows, Mac, Linux, or even your smartphone. Puter also helps you organize your work and entertainment by allowing multiple virtual desktops. This means you can keep different tasks separate but easily accessible, which helps you work more efficiently. Plus, it's open-source, so you can customize it to fit your needs.
https://github.com/HeyPuter/puter
#DL
📱
Zeus New Pytorch Ecosystem Tool
Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads.
🖥Github
-----
Main channel: @repo_science
Coupons: @freecoupons_reposcience
-----
#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