#ruby#api_documentation#app#devdocs#developer_tools#docs#documentation#documentation_tool#hacktoberfest#offline#pwa
DevDocs is a free, fast, and easy-to-use web app that lets you quickly search and browse official API documentation for many programming languages and tools all in one place. It works offline, has a clean interface, supports keyboard shortcuts, dark mode, and mobile use. You can access over 100 documentations like HTML, CSS, JavaScript, and more without switching tabs or searching Google repeatedly. It saves time by providing instant search results and keeps everything organized, making coding and learning more efficient and enjoyable. You can use it online or run it locally with Docker for even faster access[1][2][3][5].
https://github.com/freeCodeCamp/devdocs
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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