#typescript#android#appwrite#backend#backend_as_a_service#docker#firebase#flutter#hacktoberfest#hosting#ios#javascript#nextjs#react#react_native#reactnative#self_hosted#selfhosted#serverless#swift#web
Appwrite is a backend platform that helps you build web, mobile, and Flutter apps quickly and easily. It handles complex tasks like user authentication, database management, file storage, and more, so you don’t have to build these from scratch. Appwrite is open source, secure, and works with many programming languages and frameworks. You can use it in the cloud or host it yourself using Docker. The main benefit is that it saves you time and effort, letting you focus on creating great features for your app instead of worrying about backend setup and maintenance[3][5][1].
https://github.com/appwrite/appwrite
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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