#powershell#automated#bloatware#bloatware_removal#cleanup#debloat#debloater#interactive#optimize#powershell#powershell_script#privacy#ps1#registry_tweaks#tweaks#windows#windows_10#windows_11#windows_11_debloat#windows10#windows11
Win11Debloat is a free PowerShell script that quickly removes pre-installed Windows bloatware like TikTok, Xbox Game Bar, and Copilot while disabling ads, telemetry, and intrusive features. It simplifies tasks like restoring the classic right-click menu, hiding duplicate drives in File Explorer, and removing taskbar clutter, saving you from manually adjusting settings. The script is safe, reversible, and improves your experience by eliminating unnecessary background processes and distractions, making Windows cleaner and more focused.
https://github.com/Raphire/Win11Debloat
#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