#svelte#component#component_library#components#css#css_components#css_framework#daisyui#design_pattern#design_system#design_systems#postcss#svelte#tailwind#tailwind_css#tailwindcss#ui_design#ui_framework#ui_kit#ui_library#ui_pattern
daisyUI is a popular, free, and open-source component library for Tailwind CSS. It helps you build faster by providing useful class names for common UI elements like cards and calendars. This means you write less code and can focus on designing your interface more efficiently. daisyUI is also very customizable and works well with Next.js, adding no extra JavaScript to your projects, which keeps them fast and efficient. Overall, using daisyUI simplifies your development process and makes your projects more maintainable.
https://github.com/saadeghi/daisyui
#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