#javascript#3d#augmented_reality#canvas#html5#javascript#svg#virtual_reality#webaudio#webgl#webgl2#webgpu#webxr
Three.js is a powerful and easy-to-use JavaScript library that helps you create 3D graphics and animations on the web with much less code than using WebGL directly. It handles complex tasks like rendering and math calculations, so you can focus on designing your 3D scenes. It supports WebGL and WebGPU, with additional options like SVG and CSS3D. Three.js has excellent documentation, many examples, and a large, active community that provides support and updates. This makes it ideal for quickly building interactive 3D content that works across browsers, improving your web projects with engaging visuals and smooth performance[1][3][5].
https://github.com/mrdoob/three.js
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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.
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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