#rust#2d_graphics#art#compositor#design#graphic_design#graphics_editor#image_generation#image_manipulation#image_processing#node_editor#node_graph#photo_editing#photo_editor#procedural#procedural_art#procedural_drawing#svg_editor#vector_editor
Graphite is a free, open-source 2D graphics editor that combines vector and raster tools with a unique hybrid workflow using layers and nodes. It lets you create detailed vector art and designs with nondestructive editing, meaning you can change your work anytime without losing quality. The node-based system offers powerful, flexible control like visual programming, while the layer system keeps things simple and familiar. This makes it easy to create complex graphics, animations, and effects all in one tool. Graphite is still evolving but aims to be a versatile, all-in-one creative platform accessible to everyone, helping you unleash your artistic potential efficiently[1][2][4].
https://github.com/GraphiteEditor/Graphite
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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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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
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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/
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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
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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